Method, computer readable medium and apparatus for converting color image resolution

ABSTRACT

A method, computer readable medium and apparatus for converting color image resolution include inputting an image enlarging ratio, inputting target pixel data of an image in a first color space to be enlarged in a sequence, sampling reference pixels including the target pixel and pixels at least one of which links to the target pixel, and converting the reference pixel data into a second color space data. Other functions include extracting image feature quantities from the reference pixel data, selecting one of a plurality of pixel multiplying methods according to the extracted image feature quantities, multiplying the target pixel by the selected image multiplying method with an integer value based on the input image enlarging ratio, and outputting pixel data that have been generated by the target pixel multiplying step in a sequence.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method, computer readable medium andapparatus for converting color image resolution, and more particularlyrelates to a method, computer readable medium and apparatus forconverting color image resolution to convert relatively low resolutioncolor image having bi-level and multi-level pixel values into relativelyhigh resolution color image.

2. Discussion of the Background

In general, a digital image displayed on a monitor screen, such as animage of an Internet web page, has relatively low image resolution, forexample, 72 dots per inch (dpi). On the other hand, recent color imageprinters can print images with a relatively high image resolution suchas 400 dpi, 600 dpi, 1200 dpi, etc., in comparison with such the monitorscreen. In other words, a printed image can have more picture elements(pixels) than an image displayed on such the monitor screen per unitarea. Therefore, when image data for displaying on a monitor screen isdirectly printed on a sheet of paper as a hardcopy by the color imageprinter, the size of the printed image becomes smaller than that on themonitor screen. To print a hardcopy with a preferable size bycapitalizing on a high image resolution capability of those color imageprinters, a relatively low resolution image data is converted into arelatively high resolution image data.

As another example, an image taken by a digital still camera, one ofscenes taken by a video camcorder, etc., may also have a low imageresolution, and therefore such the images may also be converted intorelatively high resolution image data. Then, an image printer can printthe image substantially in a full of a paper size. A whole image on amonitor screen generally includes various categories of images, such astext and character strings, text and character strings having shadows,text and character strings processed by a so-called anti-alieningprocessing, photographs, illustrations, drawings, etc. Images are alsocategorized into bi-level data images such as ordinary text strings andmulti-level data images such as continuous toned photographs.

As background art of the field, some Japanese Laid-Open PatentPublications describe methods and devices that convert a relatively lowresolution image into a relatively high resolution image according to animage category, respectively. However, as appreciated by the presentinventers, those Patent Publications do not disclose a method forconverting an image resolution for an image structured by various typesof images with reducing a jaggy image, a coloring and a blurring in arelatively short execution time of the conversion.

SUMMARY OF THE INVENTION

The present invention has been made in view of the above-discussed andother problems and to overcome the above-discussed and other problemsassociated with the background methods and apparatuses. Accordingly, oneobject of the present invention is to provide a novel method, computerreadable medium and apparatus for converting color image resolution thatcan convert a relatively low resolution image into a relatively highresolution image with reducing a jaggy image at an image boundaryincluding a continuous toned color image.

Another object of the present invention is to provide a novel method,computer readable medium and apparatus for converting color imageresolution that can convert a relatively low resolution image into arelatively high resolution image in a relatively short time.

Still another object of the present invention is to provide a novelmethod, computer readable medium and apparatus for converting colorimage resolution that can convert a relatively low resolution image intoa relatively high resolution image with reducing a coloring and ablurring at an image boundary.

To achieve these and other objects, the present invention provides anovel method, computer readable medium and apparatus for convertingcolor image resolution include inputting an image enlarging ratio,inputting target pixel data of an image in a first color space to beenlarged in a sequence, sampling reference pixels including the targetpixel and pixels at least one of which links to the target pixel, andconverting the reference pixel data into a second color space data.Other functions include extracting image feature quantities from thereference pixel data, selecting one of a plurality of pixel multiplyingmethods according to the extracted image feature quantities, multiplyingthe target pixel by the selected image multiplying method with aninteger value based on the input image enlarging ratio, and outputtingpixel data that have been generated by the target pixel multiplying stepin a sequence.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present invention and many of theattendant advantages thereof will be readily obtained as the samebecomes better understood by reference to the following detaileddescription when considered in connection with the accompanyingdrawings, wherein:

FIG. 1 is a flowchart illustrating operational steps for practicing anexemplary color image resolution converting method according to thepresent invention;

FIG. 2 is a schematic illustration of an exemplary computer system forexecuting the color image resolution converting method of FIG. 1;

FIG. 3 is a schematic block diagram of the computer system of FIG. 2;

FIG. 4 is a graph illustrating a relationship between an input enlargingratio ER and a multiplier MR;

FIG. 5 is a flowchart illustrating operational steps for determining themultiplier MR and a correction factor CF from the input image enlargingratio ER;

FIG. 6 is an illustration of an exemplary distribution of input imagepixels and a sampling template;

FIG. 7 is a flowchart illustrating operational steps for selecting apixel multiplying method as an example according to the presentinvention;

FIG. 8 is a flowchart illustrating operational steps for selecting apixel multiplying method as another example according to the presentinvention;

FIG. 9 is a flowchart illustrating operational steps for selecting apixel multiplying method as a further example according to the presentinvention;

FIG. 10A and FIG. 10B are illustrations of examples of linking pixels;

FIG. 11 is a flowchart illustrating operational steps for practicing thepixel multiplying method 1 of FIG. 1 according to the present invention;

FIG. 12 is an illustration of input pixels and output pixels generatedby the pixel multiplying method 1 of FIG. 11;

FIG. 13 is a flowchart illustrating operational steps for practicing thepixel multiplying method 2 of FIG. 1 according to the present invention;

FIG. 14 is an illustration of input pixels and output pixels generatedby the pixel multiplying method 2 of FIG. 13;

FIG. 15A, FIG. 15B and FIG. 15C are illustrations of examples ofluminance converting characteristics used in the pixel multiplyingmethod 2 of FIG. 1;

FIG. 16 is a flowchart illustrating operational steps for converting animage luminance Y as another example practiced in the pixel multiplyingmethod 2 of FIG. 1;

FIG. 17 is a flowchart illustrating operational steps for practicing thepixel multiplying method 3 of FIG. 1 according to the present invention;

FIG. 18A is an illustration of exemplary input reference pixel data;

FIG. 18B is an illustration of bi-level pixel data converted from theinput pixel data of FIG. 18A;

FIG. 19 is an illustration of a table having pattern indexes, matchingpatters, embedding patterns and filling information;

FIG. 20A, FIG. 20B and FIG. 20C are illustrations for explainingembedding patterns filled with addressed pixel data;

FIG. 21A and FIG. 21B are illustrations for explaining output pixelpatterns filled with data of a target pixel X;

FIG. 22 is an illustration of a divided area of a template;

FIG. 23 is an illustration of a divided area of a matching pattern;

FIG. 24 is an illustration of a table of divided area values and patternindexes;

FIG. 25 is a flowchart illustrating operational steps for practicinganother example of the pixel multiplying method 3 of FIG. 1 according tothe present invention;

FIG. 26 is a flowchart illustrating operational steps for practicingstill another example of the pixel multiplying method 3 of FIG. 1according to the present invention;

FIG. 27 is a flowchart illustrating operational steps for practicing afurther example of the pixel multiplying method 3 of FIG. 1 according tothe present invention;

FIG. 28 is a flowchart illustrating operational steps for practicing thepixel multiplying method 4 of FIG. 1 according to the present invention;

FIG. 29A is an illustration of exemplary input pixel data;

FIG. 29B is an illustration of bi-level data converted from the inputpixel data of FIG. 29A with a first threshold value TH1;

FIG. 29C is an illustration of bi-level data converted from the inputpixel data of FIG. 29A with a second threshold value TH2;

FIG. 30A is an illustration of an embedding pattern determined in thepixel multiplying method 4 of FIG. 28 by using the first threshold valueTH1;

FIG. 30B is an illustration of an embedding pattern determined in thepixel multiplying method 4 of FIG. 28 by using the second thresholdvalue TH2;

FIG. 30C is an illustration of an output pixel pattern generated bymerging the embedding patterns of FIG. 30A and FIG. 30B;

FIG. 31 is a flowchart illustrating operational steps of another exampleof the pixel multiplying method 4 of FIG. 1 according to the presentinvention;

FIG. 32A to FIG. 32F are illustrations for explaining an image enlargingratio adjustment operation of FIG. 1;

FIG. 33 is a block diagram illustrating an exemplary color imageresolution converting apparatus according to the present invention;

FIG. 34 is a block diagram illustrating the second enlarger of the colorimage resolution converting apparatus of FIG. 33;

FIG. 35 is a block diagram illustrating the third enlarger of the colorimage resolution converting apparatus of FIG. 33;

FIG. 36 is a block diagram illustrating another example of the thirdenlarger of the color image resolution converting apparatus of FIG. 33;

FIG. 37 is a block diagram illustrating the fourth enlarger of the colorimage resolution converting apparatus of FIG. 33;

FIG. 38 is a block diagram illustrating another exemplary color imageresolution converting apparatus according to the present invention;

FIG. 39 is a block diagram illustrating the third enlarger of the colorimage resolution converting apparatus of FIG. 38;

FIG. 40 is a block diagram illustrating the fourth enlarger of the colorimage resolution converting apparatus of FIG. 38; and

FIG. 41 is a schematic view illustrating an image forming apparatus asan example configured according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, and moreparticularly to FIG. 1 thereof, is shown as an exemplary color imageresolution converting method according to the present invention. Thepresent exemplary method converts image resolution for various types ofimages, such as plain single color images like a background of textstrings, full color images like photographs, binary text strings anddrawings, anti-alias processed text strings and drawings, text stringsand drawings having shadows, continuous toned text strings, continuoustoned graphic images, half toned graphic images, etc.

In the present invention, a term image enlargement refers to multiplyingor increasing the number of pixels in an input image, and thereby animage having a larger number of pixels is output. A term enlarging ratioor image enlarging ratio refers to a ratio of the number of pixels of anoutput image to the number of pixels of an input image in a horizontaldirection, or a ratio of the number of pixels of the output image to thenumber of pixels of the input image in a vertical direction.

For example, an image on a 17-inch monitor screen having horizontally1024 pixels and vertically 768 pixels may be printed on a letter size(11 inches by 8.5 inches) paper as a hardcopy, for example, by a 600 dpicolor laser printer. In such the case, the letter size hardcopy can havehorizontally 6600 pixels at most and vertically 5100 pixels at most.Therefore, a horizontal enlarging ratio ER can be 6.4453 (=6600/1024) atmost and a vertical enlarging ratio ER can be 6.6406 (=5100/768) atmost. In general, the horizontal enlarging ratio ER and the verticalenlarging ratio ER are desirable to be identical to avoid a distortionof the hardcopy image in many cases. Further, a white margin is oftenpreferable at the circumference of the paper surrounding the hardcopyimage. Therefore, as an example, the enlarging ratio ER 6.4 is preferredfor outputting the hardcopy image.

The present image resolution converting method is also applied forconverting an image resolution of a conventional monitor screen to animage resolution for a high definition monitor screen. In this case, theimage enlarging ratio ER may be smaller than the above-described valuefor printing, such as a ratio 2.4. The present exemplary imageresolution converting method includes four different types of pixelmultiplying methods and an optional pixel adjustment method. The formerfour pixel multiplying methods multiply the number of pixels in anoriginal image by an integer multiple number. The pixel adjustmentmethod adjusts the pixels of multiplied image based on a fraction partof an input enlarging ratio ER. When an input enlargement ratio isdefined by only an integer value, i.e., the fraction part of the inputenlargement ratio is zero; the optional pixel adjustment operation isnot needed.

Pixels constructing a page image data, such as a monitor screen data, ashot of a digital still camera, etc., are sequentially input by onepixel by the other pixel. Every input single pixel is categorized in oneof four types of images. Then, the input pixel is adaptively multipliedby one of the four pixel multiplying methods according to the determinedcategory. When all of the pixels of the page image data are processed bythe above image categorizing step and adaptive pixel multiplying step,the image enlarging process, i.e., image resolution converting processfor the page is completed.

Each of the four pixel multiplying methods is described as follows.Method 1 applies a uniform pixel multiplying method and is customizedfor plain single color images like a background image, graphic imagesexcept image boundaries and vicinities thereof, etc. Method 2 applies abidirectional liner interpolation method for luminance data Y and iscustomized for full color images like photographs, continuous toned textstrings, continuous toned graphic images, etc. Method 3 applies apatterned pixel embedding method and is customized for binary textstrings and drawings, etc. Method 4 applies a multiple patterned pixelembedding method and is customized for anti-alias processed text stringsand drawings, text strings and drawings having shadows, etc.

Referring to FIG. 1, initially, per step S11, an image enlarging ratioER is input. In step S12, a multiplier MR and a correction factor CF aredetermined based on the input image enlarging ratio ER. The multiplierMR is an integer number, and the correction factor CF is a real number.In step S13, target pixel data of an original image, which is defined ina first color space, is input. As the first color space, for example, ared, green and blue color space may be used. The term target pixelrefers to a pixel to be processed for the following image enlargingoperation. In step S14, M×N reference pixels including the target pixelX are sampled. In step S15, the sampled M×N reference pixel data areconverted into second color space data. As the second color space, forexample, luminance data Y. color difference data I and Q may be used.

In step S16, image feature quantities or an amount of imagecharacteristics is extracted from the sampled M×N pixel data. In stepS17, one of the four pixel multiplying methods, which is describedabove, is selected according to the extracted image feature quantities.In step S18, the process branches to the selected pixel multiplyingmethod, i.e., one of the method 1 to method 4.

In one of among the step S21 to step S24, the input target pixel X ismultiplied by the multiplier MR squared by a respective pixelmultiplying method. In step S25, the multiplied pixel data is testedwhether a color conversion is needed. If YES, in step S26, themultiplied pixel data are optionally converted into another color spacedata. The another color space may be identical with the color space ofthe image data input in the step S13. In step S27, the correction factorCF, which is determined in the step S12, is tested whether of whichvalue is zero. If NO, in step S28, an adjustment of the number of pixelsbased on the correction factor CF is executed. In step S29, themultiplied pixel data are output. In step S30, whether all pixels of theoriginal image are multiplied is tested, and when NO, the processreturns to step S13. This processing loop repeats times of the number ofpixels contained in the original image data.

FIG. 2 is illustrated as an example of a computer system 100; and FIG. 3is a schematic block diagram of the system 100 for executing a colorimage resolution converting method according to an example of thepresent invention. The computer system 100 implements the method of thepresent invention, wherein a computer housing 102 (FIG. 2) houses amotherboard 104 (FIG. 2) that contains a CPU 106, a second and a thirdoptional CPUs 106B and 106C, a memory 108 (e.g., DRAM, ROM, EPROM,EEPROM, SRAM, SDRAM, and Flash RAM), a local bus 132 (FIG. 3). Themotherboard 104 also contains a video control device 110 for controllinga monitor 120, a bus control device 130, a PCI bus 134 (FIG. 3), a SCSIcontrol device 136, and a SCSI bus 138 (FIG. 3). The motherboard 104further contains a serial data port 152, a parallel data port 154 andother optional purpose logic devices (e.g., ASICs) or configurable logicdevices (e.g., GAL and reprogrammable FPGA).

A hard disk drive 112, which is changeable, a DVD drive 118, and a cardadapter 146 are connected to the SCSI bus 138 (FIG. 3). The hard diskdrive 112 and the DVD drive 118 are inserted along the arrows A1 and A2(FIG. 2) inside the computer housing 102 in use. A mouse 164 isconnected to a USB port 140, and an image scanner 166 connected to a USBport 142. A keyboard 122, a touch pad 124, a floppy disk drive 114, aLAN adapter 144, and a modem are connected to the PCI bus 134. Alsoconnected to the SCSI bus 138, the USB ports 142 and 143, or anotherports, the computer system 100 may additionally include amagneto-optical-disk drive, a tape drive, a compact disc reader/writerdrive, and a printer. Further, the computer system 100 may be connectedto a network system via the LAN adapter 144 or the modem 146.

As stated above, the system 100 includes at least one computer readablemedium. Examples of computer readable medium are hard disks 112, DVD-ROMdisks 180, DVD-RAM disks, compact disks, magneto-optical-disks, floppydisks 182, tape, PROMs (EPROM, EEPROM, Flash ROM), DRAM, SRAM, SDRAM,and etc. Stored on any one or on a combination of computer readablemedia, the present invention includes software for controlling both thehardware of the computer 100 and for enabling the computer 100 tointeract with a human user. Such software may include, but is notlimited to, device drivers, operating systems and user applications,such as development tools. Such computer readable media further includesthe computer program product of the present invention for practicing thecolor image resolution conversion. The computer code devices of presentinvention can be any interpreted or executable code mechanism, includingbut not limited to scripts, interpreters, dynamic link libraries, Javaclasses, and complete executable programs.

Attention is now turned to each of the processing steps, which aredescribed in more detail below. Referring back to FIG. 1, in step S11,the CPU 106 receives an enlarging ratio ER from, for example, thekeyboard 122 of FIG. 2. In step S12, the CPU 106 determines a multiplierMR and a correction factor CF from the input enlarging ratio ER forenlarging the input original image by two steps. The CPU 106 firstenlarges the input image using the multiplier MR in one among step S21to step S24, and adjusts the firstly enlarged image according to thecorrection factor CF in step S28. The adjustment of an image in the stepS28 is performed by an addition of a pixel to the firstly enlarged imagepixels and a deletion of a pixel from the firstly enlarged image pixels.

FIG. 4 is a graph illustrating a relationship between an input enlargingratio ER and the multiplier MR. The horizontal axis represents the inputenlarging ratio ER and the vertical axis represents the multiplier MR.The enlarging ratio ER varies from a minimum ratio two to a maximumratio ZMAX, as an example. The multiplier MR varies from two to ZM instepwise. For example, when the input enlarging ratio ER is two or morethan two and smaller than 2.5, the multiplier MR is determined as two.When the input enlarging ratio ER is 2.5 or more and smaller than 3.5,the multiplier MR is determined as three, and so on.

FIG. 5 is a flowchart illustrating operational steps for determining themultiplier MR and the correction factor CF from the input imageenlarging ratio ER. In step S12-1, the CPU 106 tests whether thefraction part of the input enlarging ratio ER is equal to or larger than0.5. If YES, the process proceeds to step S12-2; however, if NO, theprocess branches to step S12-3. In step S12-2, the CPU 106 calculatesthe integer part of the input enlarging ratio ER+1 as the multiplier MR.In the step S12-3, the CPU 106 extracts the integer part from the inputenlarging ratio ER as the multiplier MR. In step S12-4, the CPU 106calculates a quotient of the input enlarging ratio ER divided by themultiplier MR obtained in the step S12-2 or the step S12-3 as thecorrection factor CF.

Referring back to FIG. 1, in step S13, the CPU 106 receives target pixeldata, which is defined in a first color space. In this example, a red,green and blue color space is used as the first color space. The CPU 106sequentially receives the target pixel data from an original image datafile in an internal storage device, such as the hard disk drive 112, theDVD device 118, etc., one after the other. The CPU 106 may also receivethe target pixel data from an external device, such as an Internet webserver via an external communication device, such as the LAN adapter144, the modem 146, etc.

In step S14, the CPU 106 samples M×N reference pixels (horizontally Mpixels and vertically N pixels) including the target pixel X. Thesampled reference pixels may be temporarily stored, for example, in thememory 108, the hard disk drive 112, etc.

FIG. 6 is an illustration of an exemplary distribution of input imagepixels and a sampling template. Referring to FIG. 6, the input imagedenoted as IMG is structured by horizontally K pixels and vertically Jpixels, i.e., totally K×J pixels. Each of the pixels is denoted as P11,P12, P13, P14, to PJK, respectively. The target pixel X is denoted as X,and the sampling template is illustrated with thick lines and denoted asTEMP. In this example, the sampling template TEMP can sample 25reference pixels, which are denoted as R11, R12, R13, to R55 includingthe target pixel X. The sampling template TEMP may be replaced withother types of templates, such as a template having horizontally 3pixels and vertically 3 pixels, i.e., totally 9 reference pixels, atemplate having horizontally 8 pixels and vertically 6 pixels, i.e.,totally 48 reference pixels, etc.

As described above, the input pixel data is defined in the first colorspace, i.e., the red, green and blue color space, and each of the reddata, green data and blue data has 8 bits color density information, inother words, each of the red data, green data and blue data has 256different color density value.

Referring back to FIG. 1, in step S15, the CPU 106 converts the sampledM×N reference pixel data in the first color space into a second colorspace data having a luminance component and color components, such asthe National Television System Committee (NTSC) format data. The NTSCformat includes luminance data Y and color difference data I and Q. TheCPU 106 may achieve the color space conversion from the first colorspace into the second color space by calculating the followingequations.Y=a00×R+a01×G+a02×BI=a10×R+a11×G+a12×BQ=a20×R+a21×G+a22×B

Where, coefficients a00, a01, a02, a10, a11, a12, a20, a21, and a22 arereferred as color space conversion coefficients. As a numeric example,the following coefficients may be used.Y=0.30R+0.59G+0.11BI=0.60R+0.28G+0.32BQ=0.21R+0.52G+0.31B

The CPU 106 may convert the sampled M×N reference pixel data into alsoanother type of color spaces, for example, Y, U and V data of the PhaseAlternation by Line (PAL) television system, L*, a* and b* of the CIELABcolor space system, L*, u* and v* of the CIELUV color space system, X, Yand Z of the XYZ color space system, HSV color model, HLS color model,etc. As an example, A. Imamiya describes a method of color conversionfrom the red, green and blue color space into the HSV color model, HLScolor model, PAL system, YIQ system, etc., in Computer Graphics issuedin Japan on Jul. 15, 1984 on pages 622-629.

In step S16, the CPU 106 extracts image feature quantities or an amountof image characteristics from the sampled M×N reference pixel data. Inthis example, as the image feature quantities, the CPU 106 extracts suchas the number of colors in the reference pixels, the number of hues ofthe reference pixels, similarity of hues among the reference pixels, andlinking information among the reference pixels.

In step S17, the CPU 106 selects one of the plural pixel multiplyingmethods, i.e., the pixel multiplying method 1 through pixel multiplyingmethod 4, according to the extracted image feature quantities formultiplying the target pixel X. FIG. 7 is a flowchart illustratingoperational steps for selecting one of the pixel multiplying methods.

With reference to FIG. 7, in step S17-1, the CPU 106 checks whether thenumber of colors of the reference pixels is one. If YES, the processbranches to step S17-2 to select MULTIPLYING METHOD 1, and if NO, theprocess proceeds to step S17-3. In step S17-3, the CPU 106 checkswhether the number of colors of the reference pixels is two. If YES, theprocess branches to step S17-4 to select MULTIPLYING METHOD 3, and ifNO, the process proceeds to step S17-5. In step S17-5, the CPU 106checks whether the number of the colors of the reference pixels isthree. If YES, the process branches to step S17-6 to select MULTIPLYINGMETHOD 4, and if NO, the process proceeds to step S17-7. In step S17-7,the CPU 106 selects MULTIPLYING METHOD 2.

FIG. 8 is a flowchart illustrating operational steps for selecting apixel multiplying method as another example according to the presentinvention. With reference to FIG. 8, in step S17-11, the CPU 106 checkswhether the number of colors of the reference pixels is one. If YES, theprocess branches to step S17-12 to select MULTIPLYING METHOD 1, and ifNO, the process proceeds to step S17-13. In step S17-13, the CPU 106checks whether the number of colors of the reference pixels is two. IfYES, the process branches to step S17-14 to select MULTIPLYING METHOD 3,and if NO, the process proceeds to step S17-15. In step S17-15, the CPU106 checks whether the number of colors of the reference pixels isthree. If YES, the process branches to step S17-16, and if NO, theprocess proceeds to step S17-19. In step S17-16, the CPU 106 checkswhether the hues of the reference pixels are similar. If NO, the processbranches to step S17-17, and if YES, the process proceeds to stepS17-18. In step S17-17, the CPU 106 selects MULTIPLYING METHOD 3, and instep S17-18, the CPU 106 selects MULTIPLYING METHOD 2.

In step S17-19, the CPU 106 checks whether the number of colors of thereference pixels is four. If YES, the process branches to step S17-20,and if NO, the process proceeds to step S17-22. In step S17-20, the CPU106 checks whether the hues of the reference pixels are similar. If NO,the process branches to step S17-21, and if YES, the process proceeds tostep S17-22. In step S17-21, the CPU 106 selects MULTIPLYING METHOD 3,and in step S17-22, the CPU 106 selects MULTIPLYING METHOD 2.

FIG. 9 is a flowchart illustrating operational steps for selecting apixel multiplying method as a further example according to the presentinvention. With reference to FIG. 9, in step S17-31, the CPU 106 checkswhether the number of colors of the reference pixels is one. If YES, theprocess branches to step S17-32 to select MULTIPLYING METHOD 1, and ifNO, the process proceeds to step S17-33. In step S17-33, the CPU 106checks whether the number of colors of the reference pixels is two. IfYES, the process branches to step S17-34 to select MULTIPLYING METHOD 3,and if NO, the process proceeds to step S17-35. In step S17-35, the CPU106 checks whether the number of colors of the reference pixels isthree. If YES, the process branches to step S17-36, and if NO, theprocess proceeds to step S17-41.

In step S17-36, the CPU 106 checks whether the hues of the referencepixels are similar. If NO, the process branches to step S17-37 to selectMULTIPLYING METHOD 3, and if YES, the process proceeds to step S17-38.In step S17-38, the CPU 106 checks whether there are linked pixels. IfYES, the process branches to step S17-39, and if NO, the processproceeds to step S17-40.

FIG. 10A and FIG. 10B are illustrations of examples of linking pixels.In FIG. 10A, reference pixels R15, R25, R35, R45 and R55, which areshaded, are linking pixels. In FIG. 10B, the target pixel X andreference pixels R43 and R44, which are also shaded, are linking pixels.

Referring back to FIG. 9, in step S17-39, the CPU 106 selectsMULTIPLYING METHOD 4, and in step S17-40, the CPU 106 selectsMULTIPLYING METHOD 2. In step S17-41, the CPU 106 checks whether thenumber of colors of the reference pixels is four. If YES, the processbranches to step S17-42, and if NO, the process proceeds to step S17-44.In step S17-42, the CPU 106 checks whether the hues of the referencepixels are similar. If NO, the process branches to step S17-43, and ifYES, the process proceeds to step S17-44. In step S17-43, the CPU 106selects MULTIPLYING METHOD 3, and in step S17-44, the CPU 106 selectsMULTIPLYING METHOD 2.

Referring back to FIG. 1, in step S18, the CPU 106 transfer the controlto the selected multiplying method, i.e., one of the method 1 to method4.

FIG. 11 is a flowchart illustrating operational steps for practicing thepixel multiplying method 1 of FIG. 1 (step S21 of FIG. 1). Referring toFIG. 11, in step S21-1, the CPU 106 multiplies the target pixel redcomponent by the multiplier MR squared. Similarly, the CPU 106multiplies the target pixel green component in step S21-2 and multipliesthe target pixel blue component in step S21-3.

FIG. 12 is an illustration of input pixels and output pixels generatedby the pixel multiplying method 1 of FIG. 11, when the multiplier MR isthree. In FIG. 12, a target pixel X has red, green and blue components,each of which are denoted as XR, XG and XB. The target pixel X ismultiplied by the multiplier MR times, i.e., three times, in ahorizontal direction, and also multiplied by the multiplier MR times avertical direction. Thus, the nine output pixels are obtained. In thispixel multiplying method, each of the output pixels is identical withthe target pixel X, i.e., each of the output pixels has substantiallythe same red, green and blue components to those of the target pixel X.

FIG. 13 is a flowchart illustrating operational steps for practicing thepixel multiplying method 2 of FIG. 1 (step S22 of FIG. 1). Referring toFIG. 13, in step S22-1, the CPU 106 generates the multiplier MR squaredpixels. As an example, when the multiplier MR is 4, then the CPU 106generates 16 pixels. In step S22-2, the CPU 106 generates coefficientsfor a bi-directional liner interpolating operation of luminance valuesof the generated pixels. In step S22-3, the CPU 106 determines theluminance values of the generated pixels by the bi-directional linerinterpolation method.

FIG. 14 is an illustration of the input target pixel X surrounded by thereference pixels and output pixels generated by the pixel multiplyingmethod 2 of FIG. 13. In FIG. 13, the target pixel X is donated as X, thereference pixels are denoted as R11 to R55. The pixels R22, R23, R24,R32, R34, R42, R43 and R44, which are enclosed by a thick rectangleline, are referred to for determining luminance values of the generatedpixels. As an example, when the multiplier MR is four, the CPU generates16 pixels, which are referred as P00, P01, P02, P03, P10, P11, P12, P13,P20, P21, P22, P23, P30, P31, P32 and P33. Each of the luminance value Yof the generated pixels is determined as a sum of a plurality ofproducts of one of the coefficients generated in step S22-2 of FIG. 13and the luminance value Y of a neighboring reference pixel. The value ofa coefficient is proportional to the proximity of a neighboringreference pixel from the output pixel, or counter proportional to thedistance between the neighboring reference pixel and the output pixel.

As an example, for determining the luminance values of the output pixelsP00, P01, P10 and P11, the CPU 106 refers to the luminance Y ofreference pixels R22, R23 and R32, and the target pixel X. Similarly,for determining the output pixels P02, P03, P12 and P13, the CPU 106refers to the reference pixels R23, R24 and R34, and the target pixel X,for determining the output pixels P20, P21, P30 and P31, the CPU 106refers to the reference pixels R32, R42 and R43, and the target pixel X,for determining the output pixels P22, P23, P32 and P33, the CPU 106refers to the reference pixels R34, R43 and R44, and the target pixel X.

For example, the CPU 106 determines the luminance Y of the output pixelP01 in step S22-3 of FIG. 13 as follows.luminance Y of P 01=C 1×luminance Y of R 22+C 2×luminance Y of R 23+C3×luminance Y of R 32+C 4×luminance Y of X.

Where C1, C2, C3 and C4 are coefficients. As stated, each value of thecoefficients C1, C2, C3 and C4 is counter proportional to a distancefrom the output pixel P11 to the reference pixel or the target pixel Xto be multiplied. As stated, these coefficients and other coefficientsare calculated in the step S22-2 of FIG. 13. These coefficients may bestored in, for example, the memory 108, the hard disk 112, etc., and thecoefficient calculating step S22-2 for the following inputting targetpixels can be skipped otherwise the multiplier MR changes.

Further, all coefficients for all values of multiplier MR may bepreliminary calculated and stored in a storage device such as the harddisk 112, and therefore the coefficient calculating step S22-2 isomitted.

Thus the luminance value Y of each of the generated pixels isdetermined. As a method for determining the luminance Y of the generatedpixels, a so-called bi-cubic interpolation method may also be used.Generally, the bi-cubic method achieves quality image as well as thebi-directional liner interpolation method, however the bi-cubic methodmay load the CPU 106 with a relatively heavy computing operation.

Referring back to FIG. 13, in step S22-4, the CPU 106 determines colordifferences I and Q of the generated pixels by duplicating the colordifferences I and Q of the target pixel X.

As stated above, luminance values of the generated pixels are determinedby the bidirectional liner interpolation method, and color differences Iand Q of the generated pixels are simply duplicated. Therefore, theenlarged image may seem good enough because human eyes have highsensitivity for luminance Y and low sensitivity for color, and theoperation time is saved.

Referring back to FIG. 13, in steps S22-6 through S22-18, the CPU 106performs an adaptive luminance conversion for the luminance Y obtainedin the above described bi-directional liner interpolation processaccording to image feature quantities. As the image feature quantities,the CPU 106 uses a luminance range YR, which is defined as a differencebetween the maximum luminance value YMAX and the minimum luminance valueYMIN among the generated pixels.

In step S22-6, the CPU 106 calculates the luminance range YR such thatthe maximum luminance value YMAX minus the minimum luminance value YMINamong the generated pixels. In STEP S22-7, the CPU 106 calculates anormalized luminance value YP1 for all the generated pixels P00 to P33such that the normalized luminance value YP1=(YP−YMIN)/YR

Where, YP1 is a normalized luminance value of a pixel to be calculated,YP is the luminance value of the pixel, YMIN is the minimum luminancevalue among the generated pixels, and YR is the luminance range obtainedby the above step. Therefore, the normalized luminance value ranges fromvalue zero to value one.

In STEP S22-8, the CPU 106 tests whether the luminance range YR issmaller than a first threshold value TH1. The first threshold value TH1may be predetermined based on an experiment. If YES, the processbranches to step S22-9, and if NO, the process proceeds to step S22-10.In step S22-9, the CPU 106 determines a second luminance value YP2 asthe same value as the normalized luminance YP1.

In STEP S22-10, the CPU 106 tests whether the luminance range YR islarger than a second threshold value TH2. The second threshold value TH2may also be predetermined based on an experiment. If YES, the processbranches to step S22-11, and if NO, the process proceeds to step S22-14.In STEP S22-11, the CPU 106 tests whether the normalized luminance valueYP1 is smaller than 0.5. If YES, the process branches to step S22-12,and if NO, the process branches to step S22-13. In the step S22-12, theCPU 106 assigns a value zero to the second luminance value YP2, and inthe step S22-13, the CPU 106 assigns a value one to the second luminancevalue YP2.

In step S22-14 the CPU 106 tests whether the normalized luminance valueYP1 is smaller than 0.5. If YES, the process proceeds to step S22-15,and if NO, the process branches to step S22-16. In the step S22-15, theCPU 106 assigns a value (−0.6YR+1)×YP1 to the second luminance valueYP2, and in the step S22-16, the CPU 106 assigns a value(−0.6YR+1)×YP1+0.6YR1 to the second luminance value YP2.

In the step S22-17, the CPU 106 calculates an output luminance valueYPOUT by an inverse normalizing operation such that YPOUT=YP2×YR+YMIN.In the step S22-18, the CPU 106 tests whether all the luminance data Yof the generated pixels are converted into the output luminance valueYPOUT. If No, the process returns to the step S22-8, and if YES, theadaptive luminance converting process is completed.

FIG. 15A, FIG. 15B and FIG. 15C are illustrations of examples of imageluminance converting characteristics used in the pixel multiplyingmethod 2 of FIG. 1. Referring to FIG. 15A, when the luminance range YRis relatively small, i.e., YR is smaller than the first threshold valueTH1 as tested in the step S22-8, the second luminance YP2 is equal tothe normalized pixel luminance YP1. Referring to FIG. 15B, when theluminance range YR is medium, i.e., YR is larger than the firstthreshold value TH1 and smaller than the second threshold value YH2, ansecond luminance YP2 is converted from a normalized luminance YP1 suchthat of which contrast is intensified in comparison with the inputnormalized luminance YP1. Referring to FIG. 15C, when the luminancerange YR is relatively large, i.e., YR is equal to or larger than thesecond threshold value TH2, the second luminance YP2 is converted fromthe normalized luminance YP1 such that of which contrast is furtherintensified, even may converted into a bi-level value as illustrated.

The above described pixel multiplying and adaptive luminance convertingmethod can generate a smooth tone of a final image. The method decreasejaggy outlines and false outlines in the resolution converted orenlarged images as well. In addition, the above-described adaptiveluminance converting method can decrease blurred image and therebyincrease sharpness of the resolution converted images. Further, all ofthe generated pixels have an identical color difference I and Q,therefore a coloring phenomena is also decreased.

The above stated luminance converting method uses three types luminanceconverting characteristics, however types of luminance convertingcharacteristics may be increased according to the luminance range YR.FIG. 16 is a flowchart illustrating operational steps for converting animage luminance as another example practiced in the pixel multiplyingmethod 2 of FIG. 1. With referring to FIG. 16, the steps S22-6, S22-7,S22-17 and S22-18 are the same as the steps denoted as the samereference numerals in FIG. 13. In this method, a plurality of luminanceconversion tables, denoted as TABLE 1 to TABLE N, are stored in astorage device such as the hard disk 112 in the computer system of FIG.2. Each of the plurality of luminance conversion tables includes pluralsets of a normalized luminance YP1 and a second luminance YP2. Thesetypes of tables are sometimes referred as lookup tables.

In step S22-43, the CPU 106 selects a luminance conversion table fromthe plurality of luminance conversion tables TABLE 1 to TABLE Naccording to the obtained luminance range YR. In step S22-44, the CPU106 converts a normalized luminance YP1 into a second luminance YP2using the selected conversion table. The steps S22-17 and S22-18function as the same as the step denoted as the same reference numeralin FIG. 13.

Further, the CPU 106 may calculate the output luminance YPOUT using amathematical function, such as a polynomial, of the normalized luminanceYP1 instead of the conversion tables. In this case, such themathematical function may be defined as a program code and stored in thehard disk drive 112, the memory 108, etc., of the computer system 100 ofFIG. 2 and FIG. 3.

FIG. 17 is a flowchart illustrating operational steps for practicing thepixel multiplying method 3 of FIG. 1 (step S23 of FIG. 1). In stepS23-1, the CPU 106 determines a threshold value TH asTH=(YMAX+YMIN)/2

Where YMAX is the maximum luminance value and YMIN is the minimumluminance value of the reference pixels in the sampling template TEMPillustrated in FIG. 6. FIG. 18A is an illustration of exemplary inputreference pixel data. In FIG. 18A, numeral values in the cells representluminance data Y of the pixels in the sampling template TEMP. In thiscase, the maximum luminance value is 240 and the minimum luminance valueis 10, and therefore the threshold value TH is determined as 125.

Referring back to FIG. 17, in step S23-2, the CPU 106 converts theluminance data Y of the reference pixels in the template TEMP intobi-level data with the threshold value TH. When the luminance data Y islarger than the threshold value TH, the CPU 106 converts the luminancedata Y into a bi-level image density value zero, otherwise converts intoa bi-level image density value one. FIG. 18B is an illustration ofbi-level data converted from the input reference pixel data of FIG. 18A.In FIG. 18B, dark pixels having bi-level image density value one areshaded for an easy understanding.

FIG. 19 is an illustration of a table having pattern indexes, matchingpatterns, embedding patterns and filling information. A pattern index, amatching patter, an embedding pattern and filling information in a rowof the table form a group. The table contains a plurality of such thegroups, for example, 60 groups, 128 groups, and so on. In the matchingpattern column, a symbol 1 denotes a dark pixel, a symbol 0 denotes alight pixel, and a symbol “-” denotes a don't care pixel, i.e., thepixel may be either one or zero. The dark pixel is darker or lessilluminant in comparison with the light pixel.

In the embedding pattern column, each of the embedding patterns has themultiplier MR squared pixels and each of the pixels is illustrated bywhite or shaded. The shaded pixel represents a dark pixel and is darkeror less illuminant in comparison with a light pixel being illustrated inwhite. Each of the embedding patterns is optimized to suppress a jaggyimage in the enlarged image corresponding to the matching pattern in thesame row. Such the optimized embedding patterns may be obtained based onan experiment or a computer simulation.

Symbol in the light pixel column of the filling information field, suchas R32, addresses a reference pixel in the template for filling lightpixels in an embedding pattern with the addressed reference pixel data.Symbols in the dark pixel column of the filling information field, suchas R33 (=X), addresses a reference pixel in the template for fillingdark pixels in an embedding pattern with the addressed reference pixeldata.

In this example, in the embedding pattern column and in a row, only asingle embedding pattern for the multiplier MR value eight is included,as an example. However, a plurality of embedding patterns may beincluded corresponding to a plurality of required multipliers MR. Forexample, when the multipliers MR varies from a minimum multiplier ZMINto a maximum multiplier ZMAX, the embedding pattern column in a row mayincludes (ZMAX−ZMIN−1) number of embedding patterns.

Referring back to FIG. 17, in step S23-3, the CPU 106 picks out amatching pattern in the plurality of matching patterns in the table ofFIG. 19. The matching patterns are allocated in a descending order of apriority of the following comparing operation from the top row todownward. Therefore, the CPU 106 firstly picks out the matching patternin the row being indexed pattern 0. In step S23-4, the CPU 106 comparesthe bi-level data distribution obtained in the step S23-2 with thematching pattern. In step S23-5, if the bi-level data distributioncoincides with the matching pattern, the process branches to stepS23-10. However, if the bi-level data distribution is not coincidentwith the matching pattern, the process proceeds to step S23-6.

In step S23-6, the CPU 106 checks whether another matching pattern isleft in the table, and if YES, the process returns to the step S23-3. IfNO, the process proceeds to step S23-7. In step S23-7, the CPU 106multiplies the target pixel X by the multiplier MR squared. Thereby, thegenerated pixels have the same red, green and blue data as those of thetarget pixel X.

In step S23-10, the CPU 106 selects an embedding pattern and fillinginformation, which are in the same row of the table of FIG. 19 in whichthe coincided matching pattern is included. For example, when thebi-level reference pixels coincides with the matching pattern beingindexed pattern 5, the process branches to step S23-10, and where theCPU 106 selects the embedding pattern and the filling information in thesame row. The filling information includes light pixel information anddark pixel information. In step S23-11, the CPU 106 fills the lightpixels of the embedding pattern with red, green and blue data of a pixeladdressed by the light pixel information. Similarly, the CPU 106 fillsdark pixels of the embedding pattern with red, green and blue data of apixel addressed by the dark pixel information.

FIG. 20A, FIG. 20B and FIG. 20C are illustrations for explainingembedding patterns filled with addressed pixel data. For example, whenthe reference pixels including the target pixel X coincides the matchingpattern being indexed pattern 5, the CPU 106 selects the embeddingpattern and filling information in the same row. The filling light pixelis denoted as R32, and filling dark pixel is denoted as R33 (=X), whichis identical to the target pixel X. Therefore, as illustrated in FIG.20A, the red data of light pixels (illustrated in white) in theembedding pattern are filled with the red data of the reference pixelR32, and red data of the dark pixels (illustrated in shaded) in theembedding pattern are filled with the red data of the reference pixelR33. Similarly, referring to FIG. 20B, green data of the light pixels inthe embedding pattern are filled with the green data of the referencepixel R32, and green data of the dark pixels in the embedding patternare filled with the green data of the reference pixel R33. Further,referring to FIG. 20C, blue data of the light pixels in the embeddingpattern are filled with the blue data of the reference pixel R32, andblue data of the dark pixels in the embedding pattern are filled withthe blue data of the reference pixel R33.

AS another example, when the reference pixels illustrated in FIG. 18Bare input, the input pattern coincides the matching pattern beingindexed pattern 9, the CPU 106 outputs the embedding pattern in the samerow after filling light pixels with red, green and blue data of thepixel R33 (i.e., the target pixel X), dark pixels filling with red,green and blue data of the pixel R34.

FIG. 21A and FIG. 21B are illustrations for explaining output pixelpatterns filled with data of the target pixel X, which is performed instep S23-7. As described above, when the input reference pixel patterndoes not coincide any of the matching patterns in the table of FIG. 19,the CPU 106 outputs the multiplied pixels having the same red, green andblue data of those of the target pixel X. In other words, when thetarget pixel X is relatively dark as illustrated in FIG. 21A, the wholethe output pixels also become relatively dark, and when the target pixelX is relatively light as illustrated in FIG. 21B, the whole the outputpixels also become relatively light.

As described above, the output pixels are determined according to thereference pixels including the target pixel X, thereby outlines of theenlarged image becomes smooth. In addition, each of the contour lines ofthe red, green and blue image, i.e., a boundary between a relativelylight zone and a relatively dark zone in each of the red, green and bluedata changes at identical pixel locations. Thus, a coloring or ablurring at a vicinity of an outline of an image is avoided. Further,the resolution converting operation is swiftly executed.

FIG. 22 is an illustration of a divided area of a template TEMP. Ninepixels denoted b0 to b8 inside an area circumscribed by the thick lineare divided from the template TEMP. When each of the location of thereference pixels is assigned for a binary place as illustrated from bit0 denoted as b0 to bit 8 denoted as b8, a pixel data distribution givesa numeric value. In other words, a specific pixel distribution insidethe divided area corresponds to a unique binary value.

Likewise, when a matching pattern is divided into areas and each ofpixels inside the area is assigned for the same binary place, a specificpixel pattern inside the divided area corresponds to the same uniquebinary value.

FIG. 23 is an example of a divided area of a matching pattern. The biteight b8 is a don't care reference pixel, (i.e., b8 may be either one orzero), so that the divided area has a numeric value 001001111 in binary(79 in decimal) or a value 101001111 in binary (335 in decimal).

FIG. 24 is an illustration of a table of divided area values and patternindexes. The table contains plural pairs of a divided area value indecimal expression and pattern indexes. For a divided area having ninepixels, the table may contain at most 512 pairs of value and patternindex. However, the number of the matching patterns is fewer than thatin this example, therefore the table contains smaller quantity of pairsthan 512. Plural matching patterns may have an identical divided areavalue, for example, both the divided area of the matching pattern 9 andthe divided area of the matching pattern 24 have a common value 79 indecimal. Thus, in the row of divided area value 79, the pattern indexcolumn contains both pattern 9 and pattern 24.

FIG. 25 is a flowchart illustrating operational steps for practicinganother example of the pixel multiplying method 3 of FIG. 1 according tothe present invention. Referring to FIG. 25, in step S23-21, the CPU 106determines a threshold value TH as TH=(YMAX+YMIN)/2. In step S23-22, theCPU 106 converts the luminance data Y of the pixels in the template TEMPinto bi-level data with the threshold value TH. In step S22-23, the CPU106 converts a pixel bit data inside the divided area of the template,as illustrated in FIG. 22, into a numeric value. In step S22-24, the CPU106 searches the numeric value in the table of FIG. 24. In step S23-25,the CPU 106 checks whether the numeric value was found in the table. IfYES, the process proceeds to step S23-27, and if NO, the processbranches to step S23-26. In the step S23-26, the CPU 106 multiplies thetarget pixel X by the multiplier MR squared.

In step S23-27, the CPU 106 checks whether the table contains only onematching pattern index in the same row of the coincided divided areavalue exists. If YES, the process branches to step S23-31, and if NO,the process proceeds to step S23-28. In step S23-28, the CPU 106compares the pixel bi-level luminance data distribution outside thedivided area with outside the divided area of the indexed matchingpattern. In step S23-29, the CPU 106 examines the result of thecomparison, and when coincided, i.e., YES, the process branches to thestep S23-31, otherwise, the process proceeds to step S23-30. In stepS23-30, the CPU 106 picks out another indexed matching pattern in thesame row of the table of FIG. 24, and returns to the step S23-28.

In the step S23-31, the CPU 106 selects an embedding pattern and fillinginformation in the same row of the table of FIG. 19 where the coincidedmatching pattern exists. In step S23-32, the CPU 106 fills light pixelsand dark pixels of the embedding pattern with red, green and blue dataof pixels being addressed by the light pixel and the dark pixelinformation.

In this method, the pattern matching process may be executed faster thanthe method of FIG. 17; therefore the image resolution converting timemay be further shortened.

FIG. 26 is a flowchart illustrating operational steps for practicingstill another pixel multiplying method 3 of FIG. 1 according to thepresent invention. In steps S23-40 to S23-42, the CPU 106 createsembedding patterns corresponding to an input multiplier MR based onbasic embedding patterns. The basic embedding patterns are patterns fora specific multiplier MR, for example, for the multiplier MR eight ispreliminarily installed in the computer system 100 as a part of programcode a part of constant data. When a multiplier MR other than theinstalled basic embedding patterns input, the CPU creates embeddingpatterns corresponding to the input multiplier MR.

In step S23-40, the CPU 106 converts binary data of the basic embeddingpatterns into 8-bit data. In step S23-41, the CPU 106 creates primary8-bit embedding patterns corresponding to the multiplier MR. The numberof the creating embedding patterns are the same to that of the basicembedding patterns, which is also same to that of the matching patterns.For creating the primary 8-bit embedding patterns, the CPU 106 may usesuch as a bi-directional liner interpolation method. In step S23-41, theCPU 106 converts 8-bit data of the primary embedding patterns intobinary data as final embedding pattern. The created embedding patternsmay be stored in the memory 108, the hard disk drive 112, or otherstorage devices provided to the computer system 100.

In the following operational steps S23-1 to S23-11, the CPU 106 operatessubstantially the same manner as the steps denoted as the same referencenumerals in FIG. 17, therefore a description of the same steps issimplified here to avoid redundancy. In step S23-1, the CPU 106determines a threshold value TH. In step S23-2, the CPU 106 converts theluminance data Y of the pixels in the template TEMP into bi-level datawith the threshold value TH. In step S23-3, the CPU 106 picks out amatching pattern in the plurality of matching patterns in the table ofFIG. 19. In step S23-4, the CPU 106 compares the bi-level datadistribution with the matching pattern. In step S23-5, if the bi-leveldata coincides with the matching pattern, the process branches to stepS23-10, otherwise, the process proceeds to step S23-6.

In step S23-6, the CPU 106 checks whether another matching pattern isleft in the table, and if YES, the process returns to the step S23-3. IfNO, the process proceeds to step S23-7, where the CPU 106 multiplies thetarget pixel X by the multiplier MR squared. In step S23-10, the CPU 106selects an embedding pattern and filling information, which are in thesame row of the table of FIG. 19 in which the coincided matching patternis included. In step S23-11, the CPU 106 fills light and dark pixels ofthe embedding pattern with red, green and blue data of pixels addressedby the light and dark pixel information, respectively.

As described above, in this example, embedding patterns for allmultipliers are not permanently stationed in the computer system 100,therefore a capacity of a computer readable storage device is reduced.Further, installation time of the program is also reduced.

FIG. 27 is a flowchart illustrating operational steps for practicing afurther pixel multiplying method 3 of FIG. 1 according to the presentinvention. In the previously described examples, multiplied pixels ascomponents of an enlarged image have red, green and blue data, howeverin this example, multiplied pixels have luminance information and colordifference information. Referring to FIG. 27, in the followingoperational steps S23-1 to S23-6, the CPU 106 operates substantially thesame as the steps denoted as the same reference numerals of FIG. 17. Instep S23-1, the CPU 106 determines a threshold value TH. In step S23-2,the CPU 106 converts the luminance data Y of the pixels in the templateTEMP into bi-level data with the threshold value TH. In step S23-3, theCPU 106 picks out a matching pattern in the plurality of matchingpatterns in the table of FIG. 19. In step S23-4, the CPU 106 comparesthe bi-level data distribution obtained in the step S23-2 with thematching pattern.

In step S23-5, when the bi-level data distribution coincides with thematching pattern, the process branches to step S23-60. However, when thebi-level data distribution is not coincident with the matching pattern,the process proceeds to step S23-6. In step S23-6, the CPU 106 checkswhether another matching pattern is left in the table, and if YES, theprocess returns to the step S23-3. If NO, the process proceeds to stepS23-61. In step S23-61, the CPU 106 generates the multiplier MR squaredpixels. The generated pixels have the same luminance value Y as that ofthe target pixel X. In step S23-60, the CPU 106 selects an embeddingpattern, which is in the same row of the table of FIG. 19 in which thecoincided matching pattern exits. In step S23-62, the CPU 106 fills theembedding pixels with the same color difference values I and Q as thoseof the target pixel X.

FIG. 28 is a flowchart illustrating operational steps for practicing thepixel multiplying method 4 of FIG. 1 (step S24 of FIG. 1) according tothe present invention. The pixel multiplying method 4 is selected forenlarging typically, for example, text strings having shadows. Referringto FIG. 28, in step S24-1, the CPU 106 determines a first thresholdvalue TH1 and a second threshold value TH2 asTH 1=(2×YMAX+YMIN)/3TH 2=(YMAX+2×YMIN)/3

Where YMAX is the maximum luminance value, and YMIN is the minimumluminance value among the reference pixels in the template TEMPillustrated in FIG. 6.

FIG. 29A is an illustration of exemplary input reference pixel luminancevalues in the template TEMP. In the reference pixels, the maximumluminance value is 240 and the minimum luminance value is 60, thereforeafter the operation of the step S24-1, the first and second thresholdvalues TH1 and TH2 are determined as 180 and 120, respectively.

Referring back to FIG. 28, in step S24-2, the CPU 106 assigns the firstthreshold value TH1 for the following pixel multiplying operation. Instep S24-3, the CPU 106 converts the luminance data Y of the referencepixels into bi-level data with the assigned threshold value. Whenluminance data Y is larger than the assigned threshold value, the CPU106 converts the luminance data Y into a bi-level image density valuezero, otherwise converts into a bi-level image density value one.

FIG. 29B is an illustration of bi-level data converted from the inputreference pixel data of FIG. 29A with the first threshold value TH1,i.e., the value 180. In FIG. 29B, dark pixels having bi-level imagedensity value one are shaded. FIG. 29C is an illustration of bi-leveldata converted from the input reference pixel data of FIG. 29A with thesecond threshold value TH2, i.e., the value 120. Dark pixels havingbi-level image density value one are also shaded.

Referring back to FIG. 28, in step S24-4, the CPU 106 picks out amatching pattern in the plurality of matching patterns in the table ofFIG. 19. When the CPU 106 executes the step S24-4 first time, the CPU106 picks out the matching pattern in the row being indexed pattern 0 inthe table of FIG. 19. Every time the CPU 106 passes through the stepS24-4, the CPU 106 picks out the downwardly following matching patternin the table of FIG. 19. In step S24-5, the CPU 106 compares thebi-level data distribution obtained in step S24-3 with the matchingpattern. In step S24-6, when the bi-level data distribution coincideswith the matching pattern, i.e., YES, the process branches to stepS24-10. However, if the bi-level data distribution is not coincidentwith the matching pattern, i.e., NO, the process proceeds to step S24-7.

In step S24-7, the CPU 106 checks whether another matching pattern isleft in the table, and if YES, the process returns to the step S24-4. IfNO, the process proceeds to step S24-12. In step S24-10, the CPU 106selects an embedding pattern and filling information both are in thesame row of the table of FIG. 19 in which the coincided matching patternexists. FIG. 30A is an illustration of an exemplary embedding patternfor the multiplier MR value eight searched by using the first thresholdvalue TH1. FIG. 30B is an illustration of an exemplary embedding patternsearched by using the second threshold value TH2.

In step S24-11, the CPU 106 fills light pixels of the embedding patternbased on the red, green and blue data of a pixel addressed by the lightpixel information. Similarly, the CPU 106 fills dark pixels of theembedding pattern based on the red, green and blue data of a pixeladdressed by the dark pixel information. When the embedding pattern isobtained by using the first threshold value TH1, the CPU 106 fills lightpixels of the embedding pattern with ⅓ density of the red, green andblue data of a pixel addressed by the light pixel information.Similarly, the CPU 106 fills dark pixels with ⅓ density of the red,green and blue data of a pixel addressed by the dark pixel information.

When the embedding pattern is obtained by using the second thresholdvalue TH2, the CPU 106 fills light pixels of the embedding pattern with⅔ density of the red, green and blue data of a pixel addressed by thelight pixel information. Similarly, the CPU 106 fills dark pixels with ⅔density of the red, green and blue data of a pixel addressed by the darkpixel information.

In step S24-12, the CPU 106 multiplies the target pixel X by themultiplier MR squared. When the process is executed for the firstthreshold value TH1, the CPU 106 fills all the multiplied pixels with ⅓density of the red, green and blue data of the target pixel X. When theprocess is executed for the second threshold value TH2, the CPU 106fills all the multiplied pixels with ⅔ density of the red, green andblue data of the target pixel X.

In step S24-13, the CPU 106 checks whether comparing operation of thesecond bi-level data distribution with the matching patterns iscompleted, and if NO, the process branches to step S24-14. If YES, theprocess proceeds to step S24-15. In the step S24-14, the CPU 106 assignsthe second threshold value TH2 for the following operation. In stepS24-15, the CPU 106 overlays the two embedding patterns to output.

FIG. 30C is an illustration of an output pixel pattern generated bymerging operation of step S24-15 using with the embedding patterns ofFIG. 30A and FIG. 30B. As illustrated in FIG. 30C, the densities of red,green and blue change at the same location. Thus, the enlarged image isobtained with reduced jaggy images and colorings at boundaries whereimage density changes.

In the above-descried example, two sorts of threshold values are used,however more numbers of threshold values may also be used, for example,three, four, five threshold values, etc.

FIG. 31 is a flowchart illustrating operational steps for practicinganother pixel multiplying method 4 of FIG. 1. Referring to FIG. 31, instep S24-1, the CPU 106 determines a first threshold value TH1 and asecond threshold value TH2. In step S24-2, the CPU 106 assigns the firstthreshold value TH1 for the following operation. In step S24-3, the CPU106 converts the luminance data Y of the pixels in the template TEMPinto bi-level data using assigned threshold value. In step S24-4, theCPU 106 picks out a matching pattern in the plurality of matchingpatterns in the table of FIG. 19. In step S24-5, the CPU 106 comparesthe bi-level data distribution with the matching pattern picked out inthe step S24-4. In step S24-6, when the bi-level data distributioncoincides with the matching pattern, the process branches to stepS24-10, otherwise proceeds to step S24-7. In step S24-7, the CPU 106checks whether another matching pattern is left in the table, and ifYES, the process returns to the step S24-4. If NO, the process proceedsto step S24-20.

In step S24-10, the CPU 106 selects an embedding pattern and fillinginformation in the same row of the table of FIG. 19 in which thecoincided matching pattern exists. In step S24-20, the CPU 106multiplies the target pixel X by the multiplier MR squared. In stepS24-21, the CPU 106 checks whether a second matching operation with thesecond threshold value TH2 is completed, and if NO, the process branchesto step S24-22. If YES, the process proceeds to step S24-23. In the stepS24-22, the CPU 106 assigns the second threshold value TH2 for thefollowing operation.

In step S24-23, the CPU 106 overlays ⅔ image density of the secondembedding pattern obtained with the second threshold value TH2 over ⅓image density of the first embedding pattern obtained with the firstthreshold value TH1. Thus, the overlaid embedding pattern, such as theembedding pattern of FIG. 30C is obtained.

In step S24-24, the CPU 106 adds color difference data I and Q to eachof the overplayed pixels. The adding color difference data I and Q aregenerated by duplicating the color difference data of the target pixelX.

Referring back to FIG. 1, in step S25, the CPU 106 determines whether acolor conversion is needed. For example, when the enlarged image is sentto an image printer provided with the same color signal interface as thecolor generated by either the image enlarging method 1 to method 4, thecolor conversion here is not needed. However, when the enlarged imagedata is sent to an image printer provided with a different color system,such as red, green and blue signal interface, for example, the enlargedimage data is converted to the red, green and blue data. In such thecase, in step S26, the CPU 106 converts the enlarged image data in thepresent color space into another color space data. The color conversionmay be achieved in a similar manner of the step S15.

In step S27, the CPU 106 examines whether the correction factor CF iszero. When the correction factor CF is zero, the process skips step S28and proceeds to step S29, and if the correction factor CF is not zero,the process proceeds to step S26. In step S26, the CPU 106 adjust thenumber of pixels of the enlarged image according to the correctionfactor CF. When the correction factor CF is smaller than one, some ofthe multiplied pixels are deleted, and when the correction factor CF islarger than one, some pixels are added to the multiplied pixels.

FIG. 32A to FIG. 32F are illustrations for explaining an image enlargingratio adjustment operation. In the FIG. 32A to FIG. 32F, doted linesillustrate deleting pixels, and thick lines illustrate adding pixels.FIG. 32A illustrates a pixel deleting operation in a horizontaldirection for every three pixels. FIG. 32B illustrates a pixel addingoperation in the horizontal direction for every three pixels. FIG. 32Cillustrates a pixel deleting operation in a vertical direction for everythree pixels. FIG. 32D illustrates a pixel adding operation in thevertical direction for every three pixels. FIG. 32E illustrates a pixeldeleting operation in the horizontal and vertical directions for everythree pixels. FIG. 32F illustrates a pixel adding operation in thehorizontal and vertical directions for every three pixels.

As an example, when the correction factor CF is 0.666, a pixel in every3 pixels, as illustrated in FIG. 32E, is deleted, and when thecorrection factor CF is 1.333, a pixel is added for every 3 pixels, asillustrated in FIG. 32F. As another example, when the correction factorCF is 1.025, the CPU 106 inserts a pixel for every 50 pixels and a pixelfor every 200 pixels, and as a result, an output image is enlarged at aratio 1025 pixels/1000 pixels, i.e., 1.025. The inserting pixel may beidentical with the previous next pixel to the inserting pixel. Theabove-described image enlarging method is referred as a nearest neighborinterpolation method. Thus an enlarged image having substantially thesane as the input enlarging ratio ER is obtained.

In step S29, the CPU 106 outputs enlarged pixel data to, for example, adata storage device such as the hard disk drive 112, an external imageprinter through a port such as the parallel data port 154, etc. In stepS30, the CPU 106 checks whether all pixels of the original color imageare multiplied, and if NO, the process returns to the step S13 tomultiply the following pixel in the original image. When all pixels aremultiplied, whole the process is completed.

FIG. 33 is a block diagram illustrating an exemplary color imageresolution converting apparatus 300 according to the present invention.The color image resolution converting apparatus 300 includes a RGB M×Ntemplate 301, a RGB/YIQ converter 302, a YIQ M×N template 303, a featurequantity extractor 309, a switch device 307, an enlargement divider 310,a YIQ/RGB converter 311, a correction enlarger 312, a first enlarger313, a second enlarger 314, a third enlarger 315, and a fourth enlarger316. In FIG. 33, the term RGB stands for red, green and blue, and theterm YIQ stands for luminance Y, and color differences I and Q.

In this example, the first enlarger 313 applies a uniform pixelmultiplying method and is customized for plain single color images likea background image, etc. The second enlarger 314 applies abi-directional liner interpolation method for luminance data Y and iscustomized for full color images like photographs, continuous toned textstrings, continuous toned graphic images, etc. The third enlarger 315applies a patterned pixel embedding method and is customized for binarytext strings and drawings, etc. The fourth enlarger 316 applies amultiple patterned pixel embedding method and is customized foranti-alias processed text strings and drawings, text strings anddrawings having shadows, etc.

The RGB M×N template 301 and the RGB/YIQ converter 302 function as colorimage data input devices for inputting image data of a target pixel X ina first color space. The RGB M×N template 301 receives pixel data(denoted as RGB) of a color image, one by one. The RGB M×N template 301may be structured by a N line first-in first-out memory, for example.The RGB data includes red, green and blue data of a pixel. Each of thered, green and blue data are structured by, for example, 8 bit data. TheRGB M×N template 301 samples horizontally M and vertically N referencepixels including the target pixel X, and sends the sampled M×N pixelred, green and blue data, which are denoted as 301B, to the featurequantity extractor 309, the first enlarger 313, the third enlarger 315and the fourth enlarger 316. As an example, when both numerals M and Nare five, the RGB M×N template 301 stores red, green and blue of data5×5 pixels, i.e., 25 pixels such as the template TEMP of FIG. 6.

The RGB/YIQ converter 302 also receives the RGB pixel data as a firstcolor space data. The RGB/YIQ converter 302 converts the received RGBpixel data into luminance data Y, color difference data I and Q, as asecond color space data, and sends the converted data to the YIQ M×Ntemplate 303. Each of the luminance data Y, color difference data I andQ is structured by, for example, 8 bit data.

The YIQ M×N template 303 also samples horizontally M and vertically Nreference pixels including the target pixel X. Therefore, the YIQ M×Ntemplate 303 may also be structured a N line first-in first-out memory.After the sampling, the YIQ M×N template 303 sends the sampled M×N pixelluminance data Y to the feature quantity extractor 309, the secondenlarger 314, the third enlarger 315 and the fourth enlarger 316.Further, the YIQ M×N template 303 sends the sampled M×N pixel colordifference data I and Q to the second enlarger 314. As an example, whenboth numerals M and N are five, the YIQ M×N template 303 also samples 25pixels as well as the RGB pixel template 301, as referred as thetemplate TEMP illustrated in FIG. 6.

The enlargement divider 310 receives an image enlarging ratio ER anddetermines a multiplier MR and a correction factor CF based on the inputenlarging ratio ER. When the fraction part of the input enlarging ratioER is smaller than 0.5, the enlargement divider 310 determines themultiplier MR as identical with the integer part of the enlarging ratioER. When the fraction part of the input enlarging ratio ER has largervalue than 0.5, the enlargement divider 310 determines the multiplier MRas the integer part of the input enlarging ratio ER plus one. Theenlargement divider 310 determines the correction factor CF as thequotient of the input enlarging ratio ER divided by the determinedmultiplier MR.

The enlargement divider 310 sends the calculated multiplier MR to thefirst enlarger 313, the second enlarger 314, the third enlarger 315, andthe fourth enlarger 316. The enlargement divider 310 sends thecorrection factor CF to the correction enlarger 312. The product of themultiplier MR and the correction factor CF is substantially equal to theinput enlarging ratio ER. As an example, when the enlargement divider310 receives a value 8.2 as the enlarging ratio ER, the enlargementdivider 310 generates a value 8 as the multiplier MR and a value 1.025as the correction factor CF. When the enlargement divider 310 receives avalue 8.5 as the enlarging ratio ER, the enlargement divider 310generates a value 9 as the multiplier MR and a value 0.944 as thecorrection factor CF.

Each of the first enlarger 313, the second enlarger 314, the thirdenlarger 315, and the fourth enlarger 316 generates MR×MR pixels for thesingle target pixel X. For example, when the multiplier MR is 8, each ofthe enlargers generates 64 pixels, and when the multiplier MR is 9, eachof the enlargers generates 81 pixels, for the input target pixel X.

On the other hand, the correction enlarger 312 increases or decreases asingle pixel or multiple pixels for every multiple pixels that have beengenerated by either one the first, second, third and fourth enlarger313, 314, 315 and 316. As an example, when the correction enlarger 312receives the correction factor CF 1.025, the correction enlarger 312 mayinsert 25 pixels for every 1000 pixels. However, for obtaining a betterimage quality, the correction enlarger 312 may insert a pixel for every50 pixels and a pixel for every 200 pixels, and as a result, an outputimage is enlarged at a ratio 1025 pixels/1000 pixels, i.e., 1.025. Theinserting pixel may be identical with the previous next pixel to theinserting pixel. The above-described image enlarging method is referredas a nearest neighbor interpolation method.

In a sense of proportion of increased pixels, an image enlargingoperation is mainly performed by either one the first, second, third andfourth enlarger 313, 314, 315 and 316 in comparison with the correctionenlarger 312. In other words, an inserting frequency of a pixel by thenearest neighboring method is low in comparison with pixels inserted bythe first, second, third and fourth enlarger 313, 314, 315 and 316, andtherefore enlarged image quality as a whole is kept well.

The switch device 307 includes switches 307A, 307B, 307C and 307D. Eachof the switches 307A, 307B, 307C and 307D transmits each signal outputfrom the first, second, third and fourth enlarger 313, 314, 315 and 316to the correction enlarger 313 or to the YIQ/RGB converter 311,respectively.

The feature quantity extractor 309 includes a density range detector309DR, a color and hue detector 309CH and a linked pixel detector 309LP.The feature quantity extractor 309 generates a switching signal 309SW toclose one of the switches 307A, 307B, 307C and 307D. The featurequantity extractor 309 also generates an adaptive density control signal309DC to control an image density of each of the pixels generated in thesecond enlarger 314. Switching operation for the switches 307A, 307B,307C and 307D is performed per an every single target pixel X insynchronization with the target pixel X inputs.

The YIQ/RGB converter 311 converts a luminance signal Y and colordeference data I and Q that have been output from the second enlarger314 into red, green and blue data.

The first enlarger 313 multiplies the input target pixel X by themultiplier MR squared. FIG. 12 illustrates an image enlarging operationexecuted by the first enlarger 313 when the multiplier MR is three as anexample. With reference to FIG. 12, when the first enlarger 313 receivesa target pixel X having red, green and blue components, each denoted asXR, XG and XB from the RGB M×N template 301 and the multiplier MR 3 fromthe enlargement divider 310, the first enlarger 313 multiplies thetarget pixel X by 3 in a horizontal direction, and also by 3 in avertical direction.

FIG. 34 is a block diagram illustrating the second enlarger 314 of thecolor image resolution converting apparatus 300 of FIG. 33. Withreference to FIG. 34, the second enlarger 314 includes a bi-directionalliner pixel interpolator 314-1, a weighting coefficient generator 314-2,a uniform pixel interpolator 314-3, an adaptive image density converter314-4, a mixer 314-5, and a gradation character generator 314-6.

The weighting coefficient generator 314-2 receives the luminance data Yand the multiplier MR, and generates weighting coefficients forsupplying the coefficients to the bi-directional liner pixelinterpolator 314-1 according to the input data. The bi-directional linerpixel interpolator 314-1 receives the luminance data Y, the multiplierMR and the weighting coefficients, and generates the multiplier MRsquared pixels. The bi-directional liner pixel interpolator 314-1calculates luminance values for the generating pixels by abi-directional liner interpolation method using- the weightingcoefficients and the luminance data Y of neighboring pixels in thesampled pixels.

The gradation character generator 314-6 receives feature quantity 309DCfrom the feature quantity extractor 309 of FIG. 33. The gradationcharacter generator 314-6 generates a gradation convertingcharacteristic, such as such as graphs illustrated in FIG. 15A, FIG. 15Band FIG. 15C or an appropriate conversion table. The adaptive imagedensity converter 314-4 receives the luminance data Y of the generatedmultiple pixels and the gradation converting characteristic. Theadaptive image density converter 314-4 first converts each of theluminance data Y into moralized luminance data Y1. Then the adaptiveimage density converter 314-4 converts moralized luminance data Y1 intosecond luminance data Y2 according to the image feature quantity 309DC,such as graphs illustrated in FIG. 15A, FIG. 15B and FIG. 15C. Lastly,The adaptive image density converter 314-4 converts second luminancedata Y2 into output luminance data YPOUT.

The uniform pixel interpolator 314-3 generates MR by MR sets colordifference data I and Q, by duplicating of the color difference data ofthe target pixel X. The mixer mixes the adaptively density convertedluminance data YPOUT and the duplicated color difference data I and Qfor each of the generated pixels. The mixed data is output to the switch307B of FIG. 33.

FIG. 35 is a block diagram illustrating the third enlarger 315 of thecolor image resolution converting apparatus 300 of FIG. 33. The thirdenlarger 315 includes a data buffer 315-10, a pattern matching device315-2, a matching pattern memory 315-3, a uniform interpolator 315-4, apatterned interpolator 315-5, a switch 315-7, an embedding patternmemory 315-9, and a basic pattern memory 315-11, an embedding patterngenerator 315-12.

The data buffer 316-10 receives and temporally stores the red, green andblue data of the sampled reference pixels. The basic pattern memory315-11 stores a set of basic embedding patterns for a specific singlemultiplier MR, such as the embedding patterns for the multiplier MR 8 inthe table illustrated in FIG. 19.

The embedding pattern generator 315-12 receives a value of themultiplier MR. When the received multiplier MR is identical with themultiplier MR specified for the above-described basic patterns, theembedding pattern generator 315-12 duplicates the basic embeddingpatterns and transfers the duplicated embedding patterns to theembedding pattern memory 315-9. When the received multiplier MR isdifferent from the multiplier MR specified for the basic embeddingpatterns, the embedding pattern generator 315-12 generates a set ofembedding patterns corresponding to the received multiplier MR based onthe set of basic pixel patterns stored in the basic pattern memory315-11.

When the enlarging ratio ER for the horizontal direction and theenlarging ratio ER for the vertical direction have an identical value,each of the generated embedding patterns has MR pixels in bothhorizontal and vertical directions. When the enlarging ratios for bothdirections are different, the multipliers may also be different eachother. However, in both cases, the embedding pattern generator 315-12can generate the set of embedding patterns, for example, utilizing aliner interpolation method.

The pattern matching memory 315-3 stores a plurality of matchingpatterns, such as the matching patterns illustrated in the tableillustrated in FIG. 19, and supplies the matching patterns to thepattern matching device 315-2. The pattern matching device 315-2compares the bi-level reference pixel luminance data distributionreceived from the YIQ M×N template 303 of FIG. 33 with the suppliedmatching patterns in a sequence. When the bi-level reference pixelluminance data distribution coincides one of the matching patterns, thepattern matching device 315-2 outputs a coincidence signal to theembedding pattern memory 315-9 and to the switch 315-7 so as to closethe path connecting the patterned interpolator 315-5 and the outputterminal toward the switch 307C of FIG. 33. Further, the patternmatching device 315-2 sends a dark filling address and a light fillingaddress to the data buffer 315-10.

When the embedding pattern memory 315-9 receives the coincidence signal,the embedding pattern memory 315-9 sends an embedding pattern thatcorresponds to the coincided matching pattern to the patternedinterpolator 315-5. When the data buffer 315-10 received the dark andlight filling address, the data buffer 315-10 sends red, green and bluedata of the addressed pixels to the patterned interpolator 315-5. Then,the patterned interpolator 315-5 fills dark pixels in the embeddingpattern with the received red, green and blue data. Similarly, thepatterned interpolator 315-5 fills light pixels in the embedding patternwith the received red, green and blue data. Thus, the filled embeddingpattern is output to the next stage through the switch 315-7.

The uniform interpolator 315-4 unconditionally generates MR×MR pixelshaving the same red, green and blue data to those of the target pixel X.When the input pattern does not coincide any of the matching patterns,the pattern matching device 315-2 outputs a mismatch signal to theswitch 315-7 so as to close the path connecting the uniform interpolator315-4 to the output terminal. Thus, the uniformly filled embeddingpattern is output to the next stage through the switch 315-7.

FIG. 36 is a block diagram illustrating another example of the thirdenlarger 315A of the color image resolution converting apparatus 300 ofFIG. 33. In FIG. 36, the elements that are substantially the same asthose in FIG. 35 are denoted by the same reference numerals. Adescription of the same elements in FIG. 36 as in FIG. 35 is notprovided here to avoid redundancy. The third enlarger 315 includes adata buffer 315-10, a pattern matching device 315-2, a matching patternmemory 315-3, a uniform interpolator 315-4, a patterned interpolator315-5, a switch 315-7, and an embedding pattern memory 315-9A.

The embedding pattern memory 315-9A stores plural sets of embeddingpatterns. Each of the plural sets is denoted as MR2, MR3, MR4, and MZeach corresponding to a minimum multiplier MR 2 to a maximum Mz. Thenumber of sets is equal to the number of input multiplier MR. Forinstance, when the input multiplier MR varies from 2 to 30, theembedding pattern memory 315-9 stores 29 sets of embedding patterns. Ina set of embedding patterns, every embedding pattern has the same numberof pixels, and the number is equal to the multiplier MR squared.

When the embedding pattern memory 315-9A receives the coincidence signalfrom the pattern matching device 315-2, the embedding pattern memory315-9A sends an embedding pattern that corresponds to the coincidedmatching pattern and the multiplier MR to the patterned interpolator315-5.

Thus, the third enlarger 315A performs the pixel multiplying operationwithout the embedding pattern generating process of the third enlarger315 of FIG. 35.

FIG. 37 is a block diagram illustrating the fourth enlarger 316 of thecolor image resolution converting apparatus 300 of FIG. 33. The fourthenlarger includes a bi-level data converter 316-1, a data buffer 316-10,a pattern matching device 316-2, a matching pattern memory 316-3, auniform interpolator 316-4, a patterned interpolator 316-5, a merger316-6, a switch 316-7, an embedding pattern memory 316-9, a basicpattern memory 316-11, and an embedding pattern generator 316-12.

In this example, for a single target pixel X, two embedding patterns aresequentially generated, and then the merger 316-6 merges the twoembedding patterns and outputs the merged embedding pattern to the nextstage. As a first step, the bi-level data converter 316-1 receives theluminance data Y of the reference pixels inside the YIQ M×N template 303of FIG. 33. Then, the bi-level data converter 316-1 generates pluralthreshold value. In this example, the bi-level data converter 316-1generates two threshold values TH1 and TH2. Further, the bi-level dataconverter 316-1 converts the luminance data Y of the reference pixelsinto bi-level data using the first threshold value TH1. The bi-leveldata converter 316-1 then sends the converted data to the patternmatching device 316-2.

Meanwhile, the data buffer 316-10 receives the red, green and blue dataof the sampled pixels and temporally stores the red, green and blue datatherein. The basic pattern memory 316-11 stores a set of embeddingpatterns for a specific multiplier MR, such as the embedding patternsfor the multiplier MR 8, as illustrated in the table of FIG. 19.

The embedding pattern generator 316-12 receives the multiplier MR. Whenthe received multiplier MR is identical with the multiplier MRspecifying for the above-described basic patterns, the embedding patterngenerator 316-12 duplicates the basic patterns in the basic patternmemory 316-11 and sends the duplicated embedding patterns to theembedding pattern memory 316-9. When the received multiplier MR isdifferent from the multiplier MR specifying for the basic patterns, theembedding pattern generator 316-12 generates a set of embedding patternscorresponding to the received multiplier MR based on the set of basicpatterns in the basic pattern memory 316-11.

The pattern matching memory 316-3 stores a plurality of matchingpatterns, such as the matching patterns in the table illustrated in FIG.19. The pattern matching device 316-2 compares the bi-level referencepixel luminance data distribution with the matching patterns, which arereceived from the pattern matching memory 316-3, in a sequence. When thebi-level reference pixel luminance data distribution coincides one ofthe matching patterns, the pattern matching device 316-2 outputs acoincidence signal to the embedding pattern memory 316-9 and to theswitch 316-7 so as to close the path connecting the patternedinterpolator 316-5 and the merger 136-6. Further, the pattern matchingdevice 316-2 sends a dark filling address and a light filling address tothe data buffer 316-10.

The data buffer 316-10 output red, green and blue data of pixels in thedata buffer 316-1 addressed by the dark filling address and the lightfilling address to the patterned interpolator 316-5. When the embeddingpattern memory 316-9 receives the coincidence signal, the embeddingpattern memory 316-9 sends an embedding pattern, which corresponds tothe coincided matching pattern, to the patterned interpolator 316-5.When the patterned interpolator 316-5 receives the embedding pattern,the patterned interpolator 316-5 fills dark pixels of the embeddingpattern with, for example, ⅓ density of the received red, green and bluedata of the pixel addressed by the dark filling address. The patternedinterpolator 316-5 also fills light pixels of the embedding patternwith, for example, ⅓ density of the received red, green and blue data ofthe pixel addressed by the light filling address.

The uniform interpolator 316-4 first generates MR×MR pixels having, forexample, ⅓ density of the red, green and blue data of the target pixelX. When the input pattern does not coincide any of the matchingpatterns, the pattern matching device 316-2 outputs a mismatch signal tothe switch 316-7 so as to close the path connecting the uniforminterpolator 316-4. Thus, the uniformly filled embedding pattern is sentto the merger 136-6. The merger 136-6 temporally stores the receivedfirst embedding pattern output from either the patterned interpolator316-5 or the uniform interpolator 316-4.

As a second step, the bi-level data converter 316-1 converts theluminance data Y of the reference pixels into bi-level data using thesecond threshold value TH2. The bi-level data converter 316-1 then sendsthe converted data to the pattern matching device 316-2. The patternmatching device 316-2 compares the bi-level reference pixel luminancedata distribution with the matching patterns as well as the first step.When the two inputs coincide, an embedding pattern is output to themerger 136-6 in a similar manner to the first step. However, thepatterned interpolator 316-5 fills dark pixels of the embedding patternwith, for example, ⅔ density of red, green and blue data of a pixeladdressed by the dark filling address, and fills light pixels with, alsofor example, ⅔ density of red, green and blue data of a pixel addressedby the light filling address. Both the dark pixels and light pixels havedarker density in comparison with those of the first output embeddingpattern, such as twice as the above-described example. When the twoinput does not coincide, the uniform interpolator 316-4 outputs MR×MRpixels having ⅔ density of red, green and blue data of the target pixelX.

The merger 316-6 merges the firstly received embedding pattern and thesecondly received embedding pattern such that the secondly receivedembedding pattern is overlaid on the firstly received interpolation.Accordingly, lighter a pixel covered by a darker pixel becomesnon-visible.

FIG. 38 is a block diagram illustrating another exemplary color imageresolution converting apparatus 400 according to the present invention.In FIG. 38, the components that are substantially the same as those inFIG. 33 are denoted by the same reference numerals. With reference toFIG. 38, the color image resolution converting apparatus 400 includes afifth enlarger 415 and a sixth enlarger 416 instead of the thirdenlarger 315 and the fourth enlarger 316 in FIG. 33. The fifth enlarger415 and the sixth enlarger 416 receive the color difference data I and Qfrom the YIQ M×N template 303, however do not receive the red, green andblue data. The fifth enlarger 415 and the sixth enlarger 416 outputmultiplied pixel data to the YIQ/RGB converter 311 via the switch device307.

In this example, the fifth enlarger 415 applies a patterned pixelembedding method and is customized for binary text strings and drawings,etc. The sixth enlarger 416 applies a multiple patterned pixel embeddingmethod and is customized for anti-alias processed text strings anddrawings, text strings and drawings having shadows, etc.

FIG. 39 is a block diagram illustrating the fifth enlarger 415 of thecolor image resolution converting apparatus 400 of FIG. 38. Referring toFIG. 39, the fifth enlarger 415 includes a bi-level data converter415-1, a pattern matching device 415-2, a matching pattern memory 415-3,a uniform interpolator 415-4, a patterned interpolator 415-5, a switch415-7, an embedding pattern memory 415-9, a color component enlarger415-11, and a mixer 415-20.

The bi-level data converter 415-1 receives luminance data Y of referencepixels including the target pixel X inside the YIQ M×N template 303 ofFIG. 38, and converts the received data into bi-level data using athreshold value TH. The bi-level data converter 415-1 then sends theconverted bi-level data to the uniform interpolator 415-4 and thepattern matching device 415-2. The embedding pattern memory 415-9 storesplural sets of embedding patterns. Each of the plural sets is denoted asMR2, MR3, MR4, and MZ each corresponding to a minimum multiplier MR 2 toa maximum MZ. The number of sets is equal to the number of inputmultiplier MR. In a set of embedding patterns, every embedding patternhas the same number of pixels, and the number is equal to the multiplierMR squared.

When the enlarging ratio ER for a horizontal direction and the enlargingratio ER for a vertical direction are different, the number of sets ofembedding patterns is increased.

The matching pattern memory 415-3 stores a plurality of matchingpatterns, such as the matching patterns in the table illustrated in FIG.19. The matching pattern memory 415-3 supplies the plurality of matchingpatterns one by one according to a predetermined priority. The patternmatching device 415-2 compares the bi-level reference pixel luminancedata distribution, which is received from the bi-level converter 415-1with the matching patterns received from the pattern matching memory415-3. When the bi-level reference pixel luminance data distributioncoincides one of the matching patterns, the pattern matching device415-2 outputs a coincidence signal to the embedding pattern memory415-9. The pattern matching device 415-2 also outputs the coincidencesignal to the switch 415-7 to close the path connecting the patternedinterpolator 415-5 and the mixer 415-20.

Received the coincidence signal, the embedding pattern memory 415-9sends an embedding pattern, which corresponds to the coincided matchingpattern, to the patterned interpolator 415-5. Then, the patternedinterpolator 316-5 outputs the embedding pattern to the mixer 415-20 viathe closed switch 415-7.

When the bi-level reference pixel luminance data distribution does notcoincide any of the matching patterns, the pattern matching device 415-2outputs a mismatch signal to the switch 415-7 to close the pathconnecting the uniform interpolator 415-4 and the mixer 415-20. theuniform interpolator 415-4 unconditionally generates MR×MR pixels havingthe same luminance data Y of the target pixel X. Thus, the uniformlyfilled embedding pattern is output to the mixer 415-20.

The color component enlarger 415-11 generates MR×MR sets of colordifference components I and Q by duplicating those of the target pixelX, and sends the generated color difference components I and Q to themixer 415-20. The mixer 415-20 mixes the luminance data Y with the samecolor difference data I and Q, and outputs to the switch 307C of FIG.33.

FIG. 40 is a block diagram illustrating the sixth enlarger 416 of thecolor image resolution converting apparatus 400 of FIG. 38. Referring toFIG. 40, the sixth enlarger 416 includes a bi-level data converter416-1, a pattern matching device 416-2, a matching pattern memory 416-3,a uniform interpolator 416-4, a patterned interpolator 416-5, a switch416-7, an embedding pattern memory 416-9, a color component enlarger416-11, a merger 416-12, and a mixer 416-20.

In this example, for a single target pixel X, two embedding patterns aresequentially generated, and then the generated two patterns are merged.Further, the merged embedding pattern is mixed with color differencecomponent, then the merged and mixed embedding pattern is output to theswitch 307D of FIG. 38. As a first step, the bi-level data converter416-1 receives luminance data Y of the reference pixels including thetarget pixel X in the YIQ M×N template 303 of FIG. 38. Then, thebi-level data converter 416-1 generates a plurality of threshold values.In this example, the bi-level data converter 416-1 generates twothreshold values TH1 and TH2. Further, the bi-level data converter 416-1converts the luminance data Y into bi-level data using a first thresholdvalue TH1. The bi-level data converter 416-1 then sends the converteddata to the uniform interpolator 416-4 and the pattern matching device416-2.

The pattern matching device 416-2 compares the bi-level reference pixelluminance data distribution with one of the matching patterns one byone. When the input bi-level data distribution coincides one of thematching patterns, the pattern matching device 416-2 outputs acoincidence signal to the embedding pattern memory 416-9 and to theswitch 416-7 to close the path connecting the patterned interpolator416-5 and the merger 416-12.

When the embedding pattern memory 416-9 receives the coincidence signal,the embedding pattern memory 416-9 sends an embedding pattern with ⅓luminance Y of the original embedding pattern that corresponds to thecoincided matching pattern to the patterned interpolator 416-5. Then,the patterned interpolator 416-5 outputs the received embedding patternto the merger 416-12 through the switch 416-7.

The uniform interpolator 416-4 unconditionally generates MR×MR pixelshaving ⅓ luminance value of the target pixel X for each input targetpixel X for the first step. When the bi-level reference pixel luminancedata distribution does not coincide any of the matching patterns, thepattern matching device 416-2 outputs a mismatch signal to the switch416-7 to close the path connecting the uniform interpolator 416-4 andthe merger 416-12. Thus, the uniformly filled embedding pattern isoutput to the merger 416-12 through the switch 416-7. The merger 416-12temporally stores the firstly received embedding pattern.

As a second step, the bi-level data converter 416-1 converts luminancedata Y of the same reference pixels including the target pixel X intobi-level data using a second threshold value TH2, then sends theconverted data to the uniform interpolator 416-4 and the patternmatching device 416-2.

The pattern matching device 416-2 compares the bi-level reference pixelluminance data distribution with the matching patterns received from thepattern matching memory 416-3. When the bi-level reference pixelluminance data distribution coincides one of the matching patterns, thepattern matching device 416-2 outputs a coincidence signal to theembedding pattern memory 416-9 and to the switch 416-7 to close the pathconnecting the patterned interpolator 416-5 and the merger 416-12.

When the embedding pattern memory 416-9 receives the coincidence signal,the embedding pattern memory 416-9 sends an embedding pattern with ⅔luminance Y of the original embedding pattern that corresponds to thecoincided matching pattern to the patterned interpolator 416-5. Then,the patterned interpolator 416-5 outputs the second embedding pattern tothe merger 416-12 through the switch 416-7.

When the bi-level reference pixel luminance data distribution does notcoincide any of the matching patterns, the pattern matching device 416-2outputs an mismatch signal to the switch 416-7 to output MR×MR pixelshaving ⅔ luminance data Y of the second bi-level target pixel X. Thus,the uniformly filled embedding pattern is output to the merger 416-12.The merger 416-12 merges the firstly received embedding pattern and thesecondly received embedding pattern such that the secondly receivedembedding pattern is overlaid on the firstly received embedding pattern.Therefore, a lighter pixel covered by a darker pixel becomesnon-visible. The merger 416-12 then outputs the merged embedding patternto the mixer 416-20.

The color component enlarger 416-11 generates and sends color differencedata I and Q by duplicating those of the target pixel to the mixer416-20. The mixer 416-20 mixes the received luminance data Y and thecolor difference data I and Q for all the generated pixels. Then, themixed luminance Y and color difference data I and Q are output to theswitch 307D of FIG. 38. Then a next target pixel data is input to beprocessed.

FIG. 41 is a schematic view of a structure of a color image formingapparatus 800 as an example configured according to the presentinvention. The image forming apparatus 800 includes, a control module803, an operation panel 807, a photoconductor drum 810, a chargingdevice 811, a revolver color developing device 812, an image transferdevice 813, a sheet-separating device 814, a cleaning device 815, asheet drum 816, a laser scanning device 830, a sheet tray 850, a sheetfeed roller 851, a register roller pair 852, and a fixing roller pair853.

The control module 803 includes an address and data bus 803B, a networkadaptor 803N, a central processing unit (CPU) 803C, an image resolutionconverting device 803 RC, a print engine interface 803P, a randomaccesses memory (RAM) 803R, a flash memory 803F, and an input device803I. The flash memory 803F stores instruction codes executed by the CPU803C. The flash memory 803F may be replaced with another types of datastoring devices, such as a read-only memory, a hard disk, a CD-ROM, aDVD-ROM, etc. The RAM 803R may have a backup battery 803V.

The revolver color developing device 812 includes a cyan developingmodule denoted as C, a magenta developing module denoted as M, a yellowdeveloping module denoted as Y, and a black developing module denoted asK. The revolver color developing device 812 rotates clockwise so thateach of the color developing modules C, M, Y and K can face to thephotoconductive drum 810 to develop a latent image on the drum 800 withthe respective color developer.

An image forming operation is performed as the followings. The controlmodule 803 receives a print command accompanying color print data froman external apparatus, such as a personal computer, via a network andthe network adaptor 803N. When the received print command includes animage resolution converting instruction, the CPU 803C sends the receivedcolor print data and image resolution converting instruction to theimage resolution converting device 803RC. The image resolutionconverting device 803RC includes substantially the same function of theimage resolution converting apparatus 300 of FIG. 33 or image resolutionconverting apparatus 400 of FIG. 38. therefore, the image resolutionconverting device 803RC converts the input print data having arelatively low image resolution, such as 72 dots per inch, into printdata having the same image resolution of the color image formingapparatus 800, such as 600 dots per inch.

Then, the control module 803 activates a motor. The motor rotates thephotoconductive drum 810 counterclockwise. The electrical chargingdevice 811 then charges the surface of the photoconductive drum 810 at asubstantially uniform voltage. Then the CPU 803C sends first color pixeldata, such as cyan pixel data, of the resolution converted print data tothe laser scanning device 830 via the bus 803B and the print engineinterface 803P from the image resolution converting device 803RC. Thecharged photoconductive drum 810 is then exposed by a laser scanningbeam denoted as L by the laser scanning device 830 according to thereceived color pixel data. Thus, an electrostatic latent image havingrelatively high image resolution is formed on the photoconductive drum810.

After that, the developing device 812 develops the electrostatic latentimage with the first color developer, and thus a toner image, such as acyan image is formed on the photoconductive drum 810.

Meanwhile the sheet feed roller 851 and the register roll pair 852convey a sheet of paper P from the sheet tray 850 toward the sheet drum816, and the sheet drum 816 bears the sheet P on the circumferencethereof. When the toner image on the photoconductive drum 810 arrives aposition where the sheet P being carried by the sheet drum 816 and theimage transfer device 813 oppose. While the sheet P is conveyed at asubstantially same speed of the circumferential speed of thephotoconductive drum 810, a power supply supplies image transfer device813 with an appropriate voltage with the polarity of the voltage iscounter to a polarity of the electrically charged toner particles.Thereby, the first toner image on the photoconductive drum 810 isattracted toward the sheet P and transferred to the sheet P.

The toner particles remained on the photoconductive drum 810, i.e.,toner particles which have not been transferred to the sheet P, areremoved by the drum-cleaning device 815.

After the cleaning operation, the electrical charging device 811 againcharges the surface of the photoconductive drum 810 at a substantiallyuniform voltage for beginning a second color image forming. The colorimage forming processes for remaining color are repeated insubstantially the same manner as the first color image formingoperation, and thus a four color toner image is formed on the sheet Pcarried on the sheet drum 816.

The power supply supplies the sheet-separating device 814 with anappropriate voltage, such as a DC biased AC voltage. Thereby, thesheet-separating device 814 separates the sheet P from the sheet drum816. The sheet P having the transferred four color toner image isconveyed to the fixing roll pair 853 where the toner image is fixed onthe sheet P, and then the sheet P having relatively high resolutiontoner image is discharged outside the color image forming apparatus 800as a printed sheet.

As described above, the novel method, computer readable medium andapparatus for converting color image resolution that can convert arelatively low resolution image into a relatively high resolution imagewith reducing a jaggy image at an image boundary including a continuoustoned color image.

Further, the novel method, computer readable medium and apparatus forconverting color image resolution that can convert a relatively lowresolution image into a relatively high resolution image in a relativelyshort time.

Furthermore, the novel method, computer readable medium and apparatusfor converting color image resolution that can convert a relatively lowresolution image into a relatively high resolution image with reducing acoloring and a blurring at an image boundary.

Obviously, numerous modifications and variations of the presentinvention are possible in light of the above teachings. For example,features described for certain embodiments may be combined with otherembodiments described herein. It is therefore to be understood thatwithin the scope of the appended claims, the invention may be practicedotherwise than as specifically described herein.

This document is based on Japanese patent application No. 11-126021filed in the Japanese Patent Office on May 6, 1999, Japanese patentapplication No. 11-276996 filed in the Japanese Patent Office on Sep.29, 1999, and Japanese patent application No. 11-295819 filed in theJapanese Patent Office on Oct. 18, 1999, the entire contents of whichare incorporated herein by reference.

1. A method for converting color image resolution with an imageresolution converting device, comprising: inputting an image enlargingratio; inputting target pixel data of an image in a first color space tobe enlarged in a sequence; sampling, by the device, reference pixelsincluding the target pixel and pixels at least one of which links to thetarget pixel; converting, by the device, the sampled reference pixeldata in the first color space into reference pixel data in a secondcolor space data; comparing, by the device, a pixel data distribution ofthe sampled reference pixels with a plurality of matching patterns inthe second color space; outputting one of a plurality of pixel embeddingpatterns according to a coincided matching pattern and the imageenlarging ratio; generating filling color information for filing thepixels of the embedding pattern with colors according to a result of thecomparing step; and filling the pixels of the embedding pattern withcolors of the reference pixels in the first color space according to thegenerated filling color information.
 2. The method according to claim 1,wherein the first color space comprises red, green, and blue colors, andthe second color space comprises luminance information, hue information,and saturation information, and wherein the comparing step compares thepixel data distribution of the sampled reference pixels with theplurality of matching patterns in one of the luminance information, thehue information, and the saturation information.
 3. The method accordingto claim 1, wherein the first color space comprises red, green, and bluecolors, and the second color space comprises luminance information,first color difference information, and second color differenceinformation, and wherein the comparing step compares the pixel datadistribution of the sampled reference pixels with the plurality ofmatching patterns in one of the luminance information, the first colordifference information, and the second color difference information. 4.A method for converting color image resolution with an image resolutionconverting device, comprising: inputting an image enlarging ratio;inputting target pixel data of an image in a first color space to beenlarged in a sequence; sampling, by the device, reference pixelsincluding the target pixel and pixels at least one of which links to thetarget pixel; converting, by the device, the reference pixel data into apredetermined number of bi-level reference pixel data with apredetermined number of threshold values respectively; comparing, by thedevice, each pixel data distribution of the predetermined number of thebi-level reference pixel data with a plurality of matching patterns;generating a predetermined number of embedding patterns according tocoincided matching patterns and the image enlarging ratio; generating anoutput pixel pattern by merging the predetermined number of generatedembedding patterns; and outputting the generated pixel pattern.
 5. Themethod according to claim 4, further comprising: generating a pluralityof the embedding patterns based on a plurality of basic embeddingpatterns according to the input image enlarging ratio.
 6. The methodaccording to claim 4, further comprising: converting the sampledreference pixel data in the first color space into reference pixel datain a second color space data, wherein, the comparing step compares eachof the pixel data distributions of the predetermined number of thebi-level reference pixel data with the plurality of matching patterns inthe second color space, and the method further comprises: generatingfilling color information for filing the pixels of the embeddingpatterns with colors in the first color space according to a result ofthe comparing step; and filling the pixels of the predetermined numberof generated embedding patterns with the colors of the reference pixelsin the first color space according to the generated filling colorinformation.
 7. The method according to claim 4, wherein the first colorspace comprises red, green, and blue colors, and the second color spacecomprises luminance information, hue information, and saturationinformation, and wherein the comparing step compares each of the pixeldata distributions of the predetermined numbed of the bi-level referencepixel data with the plurality of matching patterns in one of theluminance information, the hue information, and the saturationinformation.
 8. The method according to claim 4, wherein the first colorspace comprises red, green and blue colors, and the second color spacecomprises luminance information, first color difference information, andsecond color difference information, and wherein the comparing stepcompares each of the pixel data distributions of the predeterminednumber of the bi-level reference pixel data with the plurality ofmatching patterns in one of the luminance information, the first colordifference information, and the second color difference information. 9.The method according to claim 4, further comprising: dividing an area ofthe sampled reference pixels into plural areas, wherein the comparingstep compares the divided pixel data distribution of the sampledreference pixels with the plurality of matching patterns.
 10. The methodaccording to claim 4, further comprising: converting the target pixeldata in the first color space into pixel data in a second color spacecomprising luminance data and color data, wherein the comparing stepcompares each of the pixel data distributions of the predeterminednumber of the bi-level reference pixel data in the second color spacewith the plurality of matching patterns, and the method furthercomprises: converting the generated output pixel pattern in the secondcolor space into an output pixel pattern in the first color space. 11.The method according to claim 10, wherein the comparing step compareseach luminance data distribution of the predetermined number of bi-levelreference pixel data with the plurality of matching patterns.
 12. Themethod according to claim 4, wherein the predetermined number ofthreshold values for converting the reference pixel data are generatedbased on the reference pixel data.
 13. The method according to claim 12,wherein the predetermined number of threshold values for converting thereference pixel data are generated based on a maximum value and aminimum value among the reference pixel data.
 14. The method accordingto claim 4, wherein the merging the embedding patterns during thegenerating step is performed such that a relatively darker pixel patternis overlaid over a relatively lighter pixel pattern.
 15. Acomputer-readable medium carrying one or more sequences of one or moreinstructions for converting color image resolution, the one or moresequences of one or more instructions including instructions which, whenexecuted by one or more processors, causes the one or more processors toperform the steps of: inputting an image enlarging ratio; inputtingtarget pixel data of an image in a first color space to be enlarged in asequence; sampling reference pixels including the target pixel andpixels at least one of which links to the target pixel; converting thereference pixel data into a predetermined number of bi-level referencepixel data with a predetermined number of threshold values respectively;comparing each pixel data distribution of the predetermined number ofthe bi-level reference pixel data with a plurality of matching patterns;generating a predetermined number of embedding patterns according tocoincided matching patterns and the image enlarging ratio; generating anoutput pixel pattern by merging the predetermined number of generatedembedding patterns; and outputting the generated output pixel pattern.16. A color image resolution converting apparatus, comprising: anenlarging ratio input device configured to input an image enlargingratio; an image data input device configured to input target pixel dataof an image in a first color space to be enlarged in a sequence; a pixelsampling device configured to sample reference pixels including thetarget pixel and pixels at least one of which links to the target pixel;a color space converting device configured to convert the sampledreference pixel data in the first color space into reference pixel datain a second color space data; a pattern comparing device configured tocompare a pixel data distribution of the sampled reference pixels with aplurality of matching patterns in the second color space; an image dataoutput device configured to output generated and selected pixel data; afilling color generating device configured to generate filling colorinformation for filing the pixels of the embedding pattern with colorsaccording to an output of the pattern comparing device; and a fillingdevice configured to fill the pixels of the embedding pattern withcolors of the reference pixels in the first color space according to thegenerated filling color information.
 17. The apparatus according toclaim 16, wherein the first color space comprises red, green and bluecolors, and the second color space comprises luminance information, hueinformation, and saturation information, and wherein the comparingdevice compares the pixel data distribution of the sampled referencepixels with the plurality of matching patterns in one of the luminanceinformation, the hue information, and the saturation information. 18.The apparatus according to claim 16, wherein the first color spacecomprises red, green, and blue colors, and the second color spacecomprises luminance information, first color difference information, andsecond color difference information, and wherein the comparing devicecompares the pixel data distribution of the sampled reference pixelswith the plurality of matching patterns in one of the luminanceinformation, the first color difference information and the second colordifference information.
 19. A color image resolution convertingapparatus, comprising: an enlarging ratio input device configured toinput an image enlarging ratio; a pixel data input device configured toinput target pixel data of an image in a first color space to beenlarged in a sequence; a pixel sampling device configured to samplereference pixels including the target pixel and pixels at least one ofwhich links to the target pixel; a bi-level data converter configured toconvert the reference pixel data into a predetermined number of bi-levelreference pixel data with a predetermined number of threshold valuesrespectively; a comparing device configured to compare each pixel datadistribution of the predetermined number of the bi-level reference pixeldata with a plurality of matching patterns; a pixel pattern generatingdevice configured to generate a predetermined number of embeddingpatterns according to coincided matching patterns and the imageenlarging ratio; a pixel pattern merging device configured to generatean output pixel pattern by merging the predetermined number of generatedembedding patterns; and an image data output device configured to outputthe generated pixel pattern.
 20. The apparatus according to claim 19,further comprising: an embedding pattern generating device configured togenerate a plurality of the embedding patterns based on a plurality ofbasic embedding patterns according to the input image enlarging ratio.21. The apparatus according to claim 19, further comprising: a colorspace converting device configured to convert the sampled referencepixel data in the first color space into reference pixel data in asecond color space data, wherein the comparing device compares each ofthe pixel data distributions of the predetermined number of the bi-levelreference pixel data with the plurality of matching patterns in thesecond color space, and the apparatus further comprises: a filling colorgenerating device configured to generate filling color information forfiling the pixels of the embedding patterns with colors in the firstcolor space according to an output of the comparing device; and a colorfilling device configured to fill the pixels of the predetermined numberof generated embedding patterns with the colors of the reference pixelsin the first color space according to the generated filling colorinformation.
 22. The apparatus according to claim 19, wherein the firstcolor space comprises red, green, and blue colors, and the second colorspace comprises luminance information, hue information, and saturationinformation, and wherein the comparing device compares each of the pixeldata distributions of the predetermined number of the bi-level referencepixel data with the plurality of matching patterns in one of theluminance information, the hue information, and the saturationinformation.
 23. The apparatus according to claim 19, wherein the firstcolor space comprises red, green, and blue colors, and the second colorspace comprises luminance information, first color differenceinformation, and second color difference information, and wherein thecomparing device compares each of the pixel data distributions of thepredetermined number of the bi-level reference pixel data with theplurality of matching patterns in one of the luminance information, thefirst color difference information, and the second color differenceinformation.
 24. The apparatus according to claim 19, furthercomprising: an area dividing device configured to divide an area of thesampled reference pixels into plural areas, wherein the patterncomparing device compares the divided pixel data distribution of thesampled reference pixels with the plurality of matching patterns. 25.The apparatus according to claim 19, further comprising: a color spaceconverting device configured to convert the target pixel data in thefirst color space into pixel data in a second color space comprisingluminance data and color data, wherein the pattern comparing devicecompares each of the pixel data distributions of the predeterminednumber of the bi-level reference pixel data in the second color spacewith the plurality of matching patterns, and the apparatus furthercomprises: a second color space converting device configured to convertthe generated output pixel pattern in the second color space into anoutput pixel pattern in the first color space.
 26. The apparatusaccording to claim 25, wherein the pattern comparing device compareseach luminance data distribution of the predetermined number of bi-levelreference pixel data with the plurality of matching patterns.
 27. Theapparatus according to claim 19, wherein the predetermined number ofthreshold values for converting the reference pixel data are generatedbased on the reference pixel data.
 28. The apparatus according to claim27, wherein the predetermined number of threshold values for convertingthe reference pixel data are generated based on a maximum value and aminimum value in the reference pixel data.
 29. The apparatus accordingto claim 19, wherein the merging the embedding patterns to generate theoutput pixel pattern is performed such that a relatively darker pixelpattern is overlaid over a relatively lighter pixel pattern.
 30. A colorimage resolution converting apparatus, comprising: means for inputtingan image enlarging ratio; means for inputting target pixel data of animage in a first color space to be enlarged in a sequence; means forsampling reference pixels including the target pixel and pixels at leastone of which links to the target pixel; means converting the referencepixel data into a predetermined number of bi-level reference pixel datawith a predetermined number of threshold values respectively; means forcomparing each pixel data distribution of the predetermined number ofthe bi-level reference pixel data with a plurality of matching patterns;means for generating a predetermined number of embedding patternsaccording to coincided matching patterns and the image enlarging ratio;generating an output pixel pattern by merging the predetermined numberof generated embedding patterns; and means for outputting the generatedoutput pixel pattern.